Journal
COMPUTER VISION AND IMAGE UNDERSTANDING
Volume 115, Issue 7, Pages 1023-1031Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2010.12.016
Keywords
Graph structure; Object recognition; Structure learning; Spectral graph theory
Funding
- EPSRC [EP/D05429X/1]
- Fundamental Research Funds for the Cental Universities
- National Laboratory of Pattern Recognition
- EPSRC [EP/D05429X/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/D05429X/1] Funding Source: researchfish
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The problem of learning the class identity of visual objects has received considerable attention recently. With rare exception, all of the work to date assumes low variation in appearance, which limits them to a single depictive style usually photographic. The same object depicted in other styles - as a drawing, perhaps - cannot be identified reliably. Yet humans are able to name the object no matter how it is depicted, and even recognize a real object having previously seen only a drawing. This paper describes a classifier which is unique in being able to learn class identity no matter how the class instances are depicted. The key to this is our proposition that topological structure is a class invariant. Practically, we depend on spectral graph analysis of a hierarchical description of an image to construct a feature vector of fixed dimension. Hence structure is transformed to a feature vector, which can be classified using standard methods. We demonstrate the classifier on several diverse classes. (C) 2011 Elsevier Inc. All rights reserved.
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