Gestalt-grouping based on path analysis for saliency detection

Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship betwee...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xu, Lijuan [verfasserIn] Ji, Zhihang [verfasserIn] Dempere-Marco, Laura [verfasserIn] Wang, Fan [verfasserIn] Hu, Xiaopeng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection

Übergeordnetes Werk:	Enthalten in: Signal processing: image communication - Amsterdam [u.a.] : Elsevier, 1989, 78, Seite 9-20
Übergeordnetes Werk:	volume:78 ; pages:9-20

DOI / URN:	10.1016/j.image.2019.05.017

Katalog-ID:	ELV002880032

Internformat


LEADER	01000caa a22002652 4500
001	ELV002880032
003	DE-627
005	20230524152028.0
007	cr uuu---uuuuu
008	230430s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.image.2019.05.017 \|2 doi
035			\|a (DE-627)ELV002880032
035			\|a (ELSEVIER)S0923-5965(18)30592-7
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|a 000 \|q DE-600
084			\|a 53.54 \|2 bkl
084			\|a 53.73 \|2 bkl
084			\|a 54.74 \|2 bkl
100	1		\|a Xu, Lijuan \|e verfasserin \|4 aut
245	1	0	\|a Gestalt-grouping based on path analysis for saliency detection
264		1	\|c 2019
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.
650		4	\|a Gestalt-grouping
650		4	\|a Smoothest path-based distance
650		4	\|a Topological connectedness
650		4	\|a Salient region detection
700	1		\|a Ji, Zhihang \|e verfasserin \|4 aut
700	1		\|a Dempere-Marco, Laura \|e verfasserin \|4 aut
700	1		\|a Wang, Fan \|e verfasserin \|4 aut
700	1		\|a Hu, Xiaopeng \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Signal processing: image communication \|d Amsterdam [u.a.] : Elsevier, 1989 \|g 78, Seite 9-20 \|h Online-Ressource \|w (DE-627)306652870 \|w (DE-600)1499759-9 \|w (DE-576)081954433 \|7 nnns
773	1	8	\|g volume:78 \|g pages:9-20
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 53.54 \|j Optoelektronik
936	b	k	\|a 53.73 \|j Nachrichtenübertragung
936	b	k	\|a 54.74 \|j Maschinelles Sehen
951			\|a AR
952			\|d 78 \|h 9-20

Indexfelder

author_variant	l x lx z j zj l d m ldm f w fw x h xh
matchkey_str	xulijuanjizhihangdemperemarcolaurawangfa:2019----:etlgopnbsdnahnlssos
hierarchy_sort_str	2019
bklnumber	53.54 53.73 54.74
publishDate	2019
allfields	10.1016/j.image.2019.05.017 doi (DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7 DE-627 ger DE-627 rda eng 004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Xu, Lijuan verfasserin aut Gestalt-grouping based on path analysis for saliency detection 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds. Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection Ji, Zhihang verfasserin aut Dempere-Marco, Laura verfasserin aut Wang, Fan verfasserin aut Hu, Xiaopeng verfasserin aut Enthalten in Signal processing: image communication Amsterdam [u.a.] : Elsevier, 1989 78, Seite 9-20 Online-Ressource (DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433 nnns volume:78 pages:9-20 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.54 Optoelektronik 53.73 Nachrichtenübertragung 54.74 Maschinelles Sehen AR 78 9-20
spelling	10.1016/j.image.2019.05.017 doi (DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7 DE-627 ger DE-627 rda eng 004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Xu, Lijuan verfasserin aut Gestalt-grouping based on path analysis for saliency detection 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds. Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection Ji, Zhihang verfasserin aut Dempere-Marco, Laura verfasserin aut Wang, Fan verfasserin aut Hu, Xiaopeng verfasserin aut Enthalten in Signal processing: image communication Amsterdam [u.a.] : Elsevier, 1989 78, Seite 9-20 Online-Ressource (DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433 nnns volume:78 pages:9-20 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.54 Optoelektronik 53.73 Nachrichtenübertragung 54.74 Maschinelles Sehen AR 78 9-20
allfields_unstemmed	10.1016/j.image.2019.05.017 doi (DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7 DE-627 ger DE-627 rda eng 004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Xu, Lijuan verfasserin aut Gestalt-grouping based on path analysis for saliency detection 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds. Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection Ji, Zhihang verfasserin aut Dempere-Marco, Laura verfasserin aut Wang, Fan verfasserin aut Hu, Xiaopeng verfasserin aut Enthalten in Signal processing: image communication Amsterdam [u.a.] : Elsevier, 1989 78, Seite 9-20 Online-Ressource (DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433 nnns volume:78 pages:9-20 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.54 Optoelektronik 53.73 Nachrichtenübertragung 54.74 Maschinelles Sehen AR 78 9-20
allfieldsGer	10.1016/j.image.2019.05.017 doi (DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7 DE-627 ger DE-627 rda eng 004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Xu, Lijuan verfasserin aut Gestalt-grouping based on path analysis for saliency detection 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds. Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection Ji, Zhihang verfasserin aut Dempere-Marco, Laura verfasserin aut Wang, Fan verfasserin aut Hu, Xiaopeng verfasserin aut Enthalten in Signal processing: image communication Amsterdam [u.a.] : Elsevier, 1989 78, Seite 9-20 Online-Ressource (DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433 nnns volume:78 pages:9-20 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.54 Optoelektronik 53.73 Nachrichtenübertragung 54.74 Maschinelles Sehen AR 78 9-20
allfieldsSound	10.1016/j.image.2019.05.017 doi (DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7 DE-627 ger DE-627 rda eng 004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Xu, Lijuan verfasserin aut Gestalt-grouping based on path analysis for saliency detection 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds. Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection Ji, Zhihang verfasserin aut Dempere-Marco, Laura verfasserin aut Wang, Fan verfasserin aut Hu, Xiaopeng verfasserin aut Enthalten in Signal processing: image communication Amsterdam [u.a.] : Elsevier, 1989 78, Seite 9-20 Online-Ressource (DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433 nnns volume:78 pages:9-20 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.54 Optoelektronik 53.73 Nachrichtenübertragung 54.74 Maschinelles Sehen AR 78 9-20
language	English
source	Enthalten in Signal processing: image communication 78, Seite 9-20 volume:78 pages:9-20
sourceStr	Enthalten in Signal processing: image communication 78, Seite 9-20 volume:78 pages:9-20
format_phy_str_mv	Article
bklname	Optoelektronik Nachrichtenübertragung Maschinelles Sehen
institution	findex.gbv.de
topic_facet	Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection
dewey-raw	004
isfreeaccess_bool	false
container_title	Signal processing: image communication
authorswithroles_txt_mv	Xu, Lijuan @@aut@@ Ji, Zhihang @@aut@@ Dempere-Marco, Laura @@aut@@ Wang, Fan @@aut@@ Hu, Xiaopeng @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	306652870
dewey-sort	14
id	ELV002880032
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV002880032</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524152028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230430s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.image.2019.05.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV002880032</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0923-5965(18)30592-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">000</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.54</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Lijuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gestalt-grouping based on path analysis for saliency detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gestalt-grouping</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smoothest path-based distance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Topological connectedness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Salient region detection</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Zhihang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dempere-Marco, Laura</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Fan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Xiaopeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Signal processing: image communication</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1989</subfield><subfield code="g">78, Seite 9-20</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306652870</subfield><subfield code="w">(DE-600)1499759-9</subfield><subfield code="w">(DE-576)081954433</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:78</subfield><subfield code="g">pages:9-20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.54</subfield><subfield code="j">Optoelektronik</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="j">Nachrichtenübertragung</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.74</subfield><subfield code="j">Maschinelles Sehen</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">78</subfield><subfield code="h">9-20</subfield></datafield></record></collection>
author	Xu, Lijuan
spellingShingle	Xu, Lijuan ddc 004 bkl 53.54 bkl 53.73 bkl 54.74 misc Gestalt-grouping misc Smoothest path-based distance misc Topological connectedness misc Salient region detection Gestalt-grouping based on path analysis for saliency detection
authorStr	Xu, Lijuan
ppnlink_with_tag_str_mv	@@773@@(DE-627)306652870
format	electronic Article
dewey-ones	004 - Data processing & computer science 000 - Computer science, information & general works
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl Gestalt-grouping based on path analysis for saliency detection Gestalt-grouping Smoothest path-based distance Topological connectedness Salient region detection
topic	ddc 004 bkl 53.54 bkl 53.73 bkl 54.74 misc Gestalt-grouping misc Smoothest path-based distance misc Topological connectedness misc Salient region detection
topic_unstemmed	ddc 004 bkl 53.54 bkl 53.73 bkl 54.74 misc Gestalt-grouping misc Smoothest path-based distance misc Topological connectedness misc Salient region detection
topic_browse	ddc 004 bkl 53.54 bkl 53.73 bkl 54.74 misc Gestalt-grouping misc Smoothest path-based distance misc Topological connectedness misc Salient region detection
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Signal processing: image communication
hierarchy_parent_id	306652870
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Signal processing: image communication
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)306652870 (DE-600)1499759-9 (DE-576)081954433
title	Gestalt-grouping based on path analysis for saliency detection
ctrlnum	(DE-627)ELV002880032 (ELSEVIER)S0923-5965(18)30592-7
title_full	Gestalt-grouping based on path analysis for saliency detection
author_sort	Xu, Lijuan
journal	Signal processing: image communication
journalStr	Signal processing: image communication
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2019
contenttype_str_mv	zzz
container_start_page	9
author_browse	Xu, Lijuan Ji, Zhihang Dempere-Marco, Laura Wang, Fan Hu, Xiaopeng
container_volume	78
class	004 000 DE-600 53.54 bkl 53.73 bkl 54.74 bkl
format_se	Elektronische Aufsätze
author-letter	Xu, Lijuan
doi_str_mv	10.1016/j.image.2019.05.017
dewey-full	004 000
author2-role	verfasserin
title_sort	gestalt-grouping based on path analysis for saliency detection
title_auth	Gestalt-grouping based on path analysis for saliency detection
abstract	Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.
abstractGer	Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.
abstract_unstemmed	Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Gestalt-grouping based on path analysis for saliency detection
remote_bool	true
author2	Ji, Zhihang Dempere-Marco, Laura Wang, Fan Hu, Xiaopeng
author2Str	Ji, Zhihang Dempere-Marco, Laura Wang, Fan Hu, Xiaopeng
ppnlink	306652870
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.image.2019.05.017
up_date	2024-07-06T17:45:42.609Z
_version_	1803852649711271936
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV002880032</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524152028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230430s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.image.2019.05.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV002880032</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0923-5965(18)30592-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">000</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.54</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Lijuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gestalt-grouping based on path analysis for saliency detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Due to the arbitrary scales, uncertain distributions of objects and cluttered background in natural scenes, uniformly detecting salient regions remains a challenge. This paper first proposes a Gestalt-grouping connectedness method based on path analysis to reflect the topological relationship between image pixels. Inspired by the Gestalt principles of feature grouping, we apply a smoothest path-based distance metric to capture the similarity, local proximity and global continuity between image pixels. The distance is small if the image pixels belong to the same visual region and large otherwise. To identify salient regions in natural images, we then propose a path-based background saliency model that integrates both the topological connectedness and appearance dissimilarity. Experimental results demonstrate the advantage of applying the path-based background saliency model in uniformly highlighting salient regions in images with complex backgrounds.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gestalt-grouping</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smoothest path-based distance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Topological connectedness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Salient region detection</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Zhihang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dempere-Marco, Laura</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Fan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Xiaopeng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Signal processing: image communication</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1989</subfield><subfield code="g">78, Seite 9-20</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306652870</subfield><subfield code="w">(DE-600)1499759-9</subfield><subfield code="w">(DE-576)081954433</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:78</subfield><subfield code="g">pages:9-20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.54</subfield><subfield code="j">Optoelektronik</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="j">Nachrichtenübertragung</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.74</subfield><subfield code="j">Maschinelles Sehen</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">78</subfield><subfield code="h">9-20</subfield></datafield></record></collection>
score	7.3981476

Nicht das Richtige dabei?

Schreiben Sie uns!

Gestalt-grouping based on path analysis for saliency detection

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?