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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 36; číslo 3; s. 453 - 465
Hlavní autoři:	Lampert, Christoph H., Nickisch, Hannes, Harmeling, Stefan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.03.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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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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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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