The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey

Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wu, Fei [verfasserIn] Han, Yahong [verfasserIn] Liu, Xiang [verfasserIn] Shao, Jian [verfasserIn] Zhuang, Yueting [verfasserIn] Zhang, Zhongfei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Structural sparsity Factor decomposition Latent structure Hashing index

Übergeordnetes Werk:	Enthalten in: International journal of multimedia information retrieval - London : Springer, 2012, 1(2012), 1 vom: 02. März, Seite 3-15
Übergeordnetes Werk:	volume:1 ; year:2012 ; number:1 ; day:02 ; month:03 ; pages:3-15

Links:	Volltext

DOI / URN:	10.1007/s13735-012-0001-9

Katalog-ID:	SPR032213212

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allfields	10.1007/s13735-012-0001-9 doi (DE-627)SPR032213212 (SPR)s13735-012-0001-9-e DE-627 ger DE-627 rakwb eng 004 660 070 020 ASE Wu, Fei verfasserin aut The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques. Structural sparsity (dpeaa)DE-He213 Factor decomposition (dpeaa)DE-He213 Latent structure (dpeaa)DE-He213 Hashing index (dpeaa)DE-He213 Han, Yahong verfasserin aut Liu, Xiang verfasserin aut Shao, Jian verfasserin aut Zhuang, Yueting verfasserin aut Zhang, Zhongfei verfasserin aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 1(2012), 1 vom: 02. März, Seite 3-15 (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:1 year:2012 number:1 day:02 month:03 pages:3-15 https://dx.doi.org/10.1007/s13735-012-0001-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2012 1 02 03 3-15
spelling	10.1007/s13735-012-0001-9 doi (DE-627)SPR032213212 (SPR)s13735-012-0001-9-e DE-627 ger DE-627 rakwb eng 004 660 070 020 ASE Wu, Fei verfasserin aut The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques. Structural sparsity (dpeaa)DE-He213 Factor decomposition (dpeaa)DE-He213 Latent structure (dpeaa)DE-He213 Hashing index (dpeaa)DE-He213 Han, Yahong verfasserin aut Liu, Xiang verfasserin aut Shao, Jian verfasserin aut Zhuang, Yueting verfasserin aut Zhang, Zhongfei verfasserin aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 1(2012), 1 vom: 02. März, Seite 3-15 (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:1 year:2012 number:1 day:02 month:03 pages:3-15 https://dx.doi.org/10.1007/s13735-012-0001-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2012 1 02 03 3-15
allfields_unstemmed	10.1007/s13735-012-0001-9 doi (DE-627)SPR032213212 (SPR)s13735-012-0001-9-e DE-627 ger DE-627 rakwb eng 004 660 070 020 ASE Wu, Fei verfasserin aut The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques. Structural sparsity (dpeaa)DE-He213 Factor decomposition (dpeaa)DE-He213 Latent structure (dpeaa)DE-He213 Hashing index (dpeaa)DE-He213 Han, Yahong verfasserin aut Liu, Xiang verfasserin aut Shao, Jian verfasserin aut Zhuang, Yueting verfasserin aut Zhang, Zhongfei verfasserin aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 1(2012), 1 vom: 02. März, Seite 3-15 (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:1 year:2012 number:1 day:02 month:03 pages:3-15 https://dx.doi.org/10.1007/s13735-012-0001-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2012 1 02 03 3-15
allfieldsGer	10.1007/s13735-012-0001-9 doi (DE-627)SPR032213212 (SPR)s13735-012-0001-9-e DE-627 ger DE-627 rakwb eng 004 660 070 020 ASE Wu, Fei verfasserin aut The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques. Structural sparsity (dpeaa)DE-He213 Factor decomposition (dpeaa)DE-He213 Latent structure (dpeaa)DE-He213 Hashing index (dpeaa)DE-He213 Han, Yahong verfasserin aut Liu, Xiang verfasserin aut Shao, Jian verfasserin aut Zhuang, Yueting verfasserin aut Zhang, Zhongfei verfasserin aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 1(2012), 1 vom: 02. März, Seite 3-15 (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:1 year:2012 number:1 day:02 month:03 pages:3-15 https://dx.doi.org/10.1007/s13735-012-0001-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2012 1 02 03 3-15
allfieldsSound	10.1007/s13735-012-0001-9 doi (DE-627)SPR032213212 (SPR)s13735-012-0001-9-e DE-627 ger DE-627 rakwb eng 004 660 070 020 ASE Wu, Fei verfasserin aut The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques. Structural sparsity (dpeaa)DE-He213 Factor decomposition (dpeaa)DE-He213 Latent structure (dpeaa)DE-He213 Hashing index (dpeaa)DE-He213 Han, Yahong verfasserin aut Liu, Xiang verfasserin aut Shao, Jian verfasserin aut Zhuang, Yueting verfasserin aut Zhang, Zhongfei verfasserin aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 1(2012), 1 vom: 02. März, Seite 3-15 (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:1 year:2012 number:1 day:02 month:03 pages:3-15 https://dx.doi.org/10.1007/s13735-012-0001-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 1 2012 1 02 03 3-15
language	English
source	Enthalten in International journal of multimedia information retrieval 1(2012), 1 vom: 02. März, Seite 3-15 volume:1 year:2012 number:1 day:02 month:03 pages:3-15
sourceStr	Enthalten in International journal of multimedia information retrieval 1(2012), 1 vom: 02. März, Seite 3-15 volume:1 year:2012 number:1 day:02 month:03 pages:3-15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Structural sparsity Factor decomposition Latent structure Hashing index
dewey-raw	004
isfreeaccess_bool	false
container_title	International journal of multimedia information retrieval
authorswithroles_txt_mv	Wu, Fei @@aut@@ Han, Yahong @@aut@@ Liu, Xiang @@aut@@ Shao, Jian @@aut@@ Zhuang, Yueting @@aut@@ Zhang, Zhongfei @@aut@@
publishDateDaySort_date	2012-03-02T00:00:00Z
hierarchy_top_id	684967537
dewey-sort	14
id	SPR032213212
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">SPR032213212</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519184118.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13735-012-0001-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR032213212</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13735-012-0001-9-e</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">rakwb</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">660</subfield><subfield code="a">070</subfield><subfield code="a">020</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. 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title	The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey
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title_full	The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey
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title_sort	heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey
title_auth	The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey
abstract	Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques.
abstractGer	Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques.
abstract_unstemmed	Abstract There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques.
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title_short	The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey
url	https://dx.doi.org/10.1007/s13735-012-0001-9
remote_bool	true
author2	Han, Yahong Liu, Xiang Shao, Jian Zhuang, Yueting Zhang, Zhongfei
author2Str	Han, Yahong Liu, Xiang Shao, Jian Zhuang, Yueting Zhang, Zhongfei
ppnlink	684967537
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doi_str	10.1007/s13735-012-0001-9
up_date	2024-07-04T02:46:00.559Z
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The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey

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