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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 36; H. 3; S. 453 - 465
Hauptverfasser: Lampert, Christoph H., Nickisch, Hannes, Harmeling, Stefan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.03.2014
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2013.140