Attribute-Based Classification for Zero-Shot Visual Object Categorization

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of differe...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 36; číslo 3; s. 453 - 465
Hlavní autori:	Lampert, Christoph H., Nickisch, Hannes, Harmeling, Stefan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.03.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Animals Applied sciences Artificial intelligence Classification Classification - methods Computer science; control theory; systems Computer vision Databases, Factual Exact sciences and technology Image Processing, Computer-Assisted - methods Learning Marine animals Models, Statistical Object recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Probabilistic logic Programming languages Semantics Software Support Vector Machine Tasks Training Vectors vision and scene understanding Computer vision Zero-shot learning Image interpretation Image databank Pattern recognition Object recognition Annotation Semantics Scene analysis Animal vision and scene understanding Object oriented Collection Categorization Image classification
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.
AbstractList	We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes. We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.
Author	Nickisch, Hannes Harmeling, Stefan Lampert, Christoph H.
Author_xml	– sequence: 1 givenname: Christoph H. surname: Lampert fullname: Lampert, Christoph H. email: chl@ist.ac.at organization: Inst. of Sci. & Technol. Austria, Klosterneuburg, Austria – sequence: 2 givenname: Hannes surname: Nickisch fullname: Nickisch, Hannes email: hannes@nickisch.org organization: Philips Res., Hamburg, Germany – sequence: 3 givenname: Stefan surname: Harmeling fullname: Harmeling, Stefan email: stefan.harmeling@tuebingen.mpg.de organization: Max Planck Inst. for Intell. Syst., Tubingen, Germany
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28402986$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24457503$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1109/TPAMI.2007.1055 10.7551/mitpress/1602.001.0001 10.1561/0600000027 10.1109/CVPR.2000.855856 10.1007/978-3-642-15549-9_48 10.5244/C.22.80 10.1109/CVPR.2012.6248021 10.1145/1178677.1178722 10.1109/TPAMI.2006.79 10.1613/jair.105 10.7551/mitpress/1113.003.0008 10.1109/CVPR.2010.5539970 10.1109/TPAMI.2011.48 10.1006/jcss.1997.1504 10.1006/cogp.1993.1006 10.1162/neco.1994.6.2.181 10.1109/CVPR.2007.383198 10.1109/ICCV.2005.148 10.21236/ada164453 10.1109/CVPR.2008.4587658 10.1109/ICCV.2011.6126281 10.1109/CVPR.2005.117 10.1109/CVPR.2012.6248026 10.1007/978-3-642-15549-9_56 10.1109/CVPR.2003.1211479 10.1109/TIP.2009.2019809 10.1007/978-3-642-33712-3_26 10.1109/CVPR.2011.5995368 10.7551/mitpress/4175.001.0001 10.1109/CVPR.2007.383157 10.1109/CVPR.2009.5206772 10.1007/BF01079209 10.5244/C.23.2 10.1007/978-3-642-33715-4_18 10.1109/CVPR.2011.5995353 10.1109/CVPR.2012.6247998 10.1109/CVPR.2005.177 10.1038/nn1794 10.1016/j.cviu.2007.09.014 10.1109/CVPR.2011.5995466 10.1109/TPAMI.2012.256 10.1109/CVPR.2011.5995451 10.1109/CVPR.2009.5206594 10.2307/2325085 10.1207/s15516709cog1502_3 10.1109/T-C.1973.223602 10.1007/978-3-540-88693-8_25 10.1007/978-3-642-15555-0_12 10.1109/CVPR.2010.5540121 10.1145/1101149.1101241 10.1037/0033-295X.94.2.115 10.1007/978-3-642-37331-2_1 10.1109/CVPR.2008.4587597 10.1023/b:visi.0000029664.99615.94 10.1145/1991996.1992014 10.7551/mitpress/8291.001.0001 10.1145/1282280.1282340 10.1007/978-3-642-15555-0_10 10.1109/CVPR.2010.5540053 10.1007/978-3-642-35749-7_5
ContentType	Journal Article
Copyright	2015 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2014
Copyright_xml	– notice: 2015 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2014
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References	ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref16 ref19 ref18 Platt (ref23) Kemp (ref57) ref51 ref50 Ferrari (ref32) ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Breiman (ref13) 1984 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref2 ref1 ref39 ref38 Rifkin (ref17) 2004; 5 ref24 ref67 ref26 ref25 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 Larochelle (ref29); 1 ref27 ref60 ref62 ref61
References_xml	– ident: ref24 doi: 10.1109/TPAMI.2007.1055 – ident: ref8 doi: 10.7551/mitpress/1602.001.0001 – ident: ref4 doi: 10.1561/0600000027 – volume-title: Proc. Advances in Neural Information Processing Systems (NIPS) ident: ref23 article-title: Large Margin DAGs for Multiclass Classification – ident: ref26 doi: 10.1109/CVPR.2000.855856 – ident: ref40 doi: 10.1007/978-3-642-15549-9_48 – ident: ref25 doi: 10.5244/C.22.80 – ident: ref44 doi: 10.1109/CVPR.2012.6248021 – volume-title: Proc. Advances in Neural Information Processing Systems (NIPS) ident: ref32 article-title: Learning Visual Attributes – ident: ref33 doi: 10.1145/1178677.1178722 – ident: ref27 doi: 10.1109/TPAMI.2006.79 – ident: ref16 doi: 10.1613/jair.105 – ident: ref63 doi: 10.7551/mitpress/1113.003.0008 – ident: ref62 doi: 10.1109/CVPR.2010.5539970 – volume-title: Classification and Regression Trees year: 1984 ident: ref13 – ident: ref35 doi: 10.1109/TPAMI.2011.48 – ident: ref15 doi: 10.1006/jcss.1997.1504 – ident: ref54 doi: 10.1006/cogp.1993.1006 – ident: ref14 doi: 10.1162/neco.1994.6.2.181 – ident: ref61 doi: 10.1109/CVPR.2007.383198 – ident: ref19 doi: 10.1109/ICCV.2005.148 – ident: ref12 doi: 10.21236/ada164453 – volume: 5 start-page: 101 year: 2004 ident: ref17 article-title: In Defense of One-vs -All Classification publication-title: J. Machine Learning Research – ident: ref58 doi: 10.1109/CVPR.2008.4587658 – volume-title: Proc. Nat’l Conf. Artificial Intelligence (AAAI) ident: ref57 article-title: Learning Systems of Concepts with an Infinite Relational Model – ident: ref10 doi: 10.1109/ICCV.2011.6126281 – ident: ref28 doi: 10.1109/CVPR.2005.117 – ident: ref51 doi: 10.1109/CVPR.2012.6248026 – ident: ref64 doi: 10.1007/978-3-642-15549-9_56 – ident: ref21 doi: 10.1109/CVPR.2003.1211479 – ident: ref31 doi: 10.1109/TIP.2009.2019809 – ident: ref52 doi: 10.1007/978-3-642-33712-3_26 – ident: ref7 doi: 10.1109/CVPR.2011.5995368 – ident: ref3 doi: 10.7551/mitpress/4175.001.0001 – ident: ref18 doi: 10.1109/CVPR.2007.383157 – ident: ref45 doi: 10.1109/CVPR.2009.5206772 – ident: ref53 doi: 10.1007/BF01079209 – ident: ref39 doi: 10.5244/C.23.2 – ident: ref37 doi: 10.1007/978-3-642-33715-4_18 – ident: ref47 doi: 10.1109/CVPR.2011.5995353 – ident: ref46 doi: 10.1109/CVPR.2012.6247998 – ident: ref2 doi: 10.1109/CVPR.2005.177 – ident: ref55 doi: 10.1038/nn1794 – ident: ref60 doi: 10.1016/j.cviu.2007.09.014 – ident: ref50 doi: 10.1109/CVPR.2011.5995466 – ident: ref66 doi: 10.1109/TPAMI.2012.256 – ident: ref36 doi: 10.1109/CVPR.2011.5995451 – ident: ref9 doi: 10.1109/CVPR.2009.5206594 – ident: ref11 doi: 10.2307/2325085 – ident: ref56 doi: 10.1207/s15516709cog1502_3 – ident: ref20 doi: 10.1109/T-C.1973.223602 – ident: ref34 doi: 10.1007/978-3-540-88693-8_25 – ident: ref42 doi: 10.1007/978-3-642-15555-0_12 – ident: ref49 doi: 10.1109/CVPR.2010.5540121 – ident: ref38 doi: 10.1145/1101149.1101241 – ident: ref6 doi: 10.1037/0033-295X.94.2.115 – ident: ref67 doi: 10.1007/978-3-642-37331-2_1 – ident: ref22 doi: 10.1109/CVPR.2008.4587597 – ident: ref1 doi: 10.1023/b:visi.0000029664.99615.94 – ident: ref48 doi: 10.1145/1991996.1992014 – ident: ref5 doi: 10.7551/mitpress/8291.001.0001 – ident: ref30 doi: 10.1145/1101149.1101241 – ident: ref59 doi: 10.1145/1282280.1282340 – ident: ref43 doi: 10.1007/978-3-642-15555-0_10 – volume: 1 start-page: 646 issue: 2 volume-title: Proc. 23rd Nat’l Conf. Artificial Intelligence ident: ref29 article-title: Zero-Data Learning of New Tasks – ident: ref65 doi: 10.1109/CVPR.2010.5540053 – ident: ref41 doi: 10.1007/978-3-642-35749-7_5
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Snippet	We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This...
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SubjectTerms	Animals Applied sciences Artificial intelligence Classification Classification - methods Computer science; control theory; systems Computer vision Databases, Factual Exact sciences and technology Image Processing, Computer-Assisted - methods Learning Marine animals Models, Statistical Object recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Probabilistic logic Programming languages Semantics Software Support Vector Machine Tasks Training Vectors vision and scene understanding
Title	Attribute-Based Classification for Zero-Shot Visual Object Categorization
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