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
Autor*in: |
Xu, Lijuan [verfasserIn] Ji, Zhihang [verfasserIn] Dempere-Marco, Laura [verfasserIn] Wang, Fan [verfasserIn] Hu, Xiaopeng [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Signal processing: image communication - Amsterdam [u.a.] : Elsevier, 1989, 78, Seite 9-20 |
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Übergeordnetes Werk: |
volume:78 ; pages:9-20 |
DOI / URN: |
10.1016/j.image.2019.05.017 |
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Katalog-ID: |
ELV002880032 |
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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 | |
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936 | b | k | |a 53.54 |j Optoelektronik |
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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 |
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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 |
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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 |
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Gestalt-grouping based on path analysis for saliency detection |
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Gestalt-grouping based on path analysis for saliency detection |
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gestalt-grouping based on path analysis for saliency detection |
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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. |
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