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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Learning Visual Object Categories and Their Composition Based on a Probabilistic Latent Variable Model
verfasst von : Masayasu Atsumi
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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This paper addresses the problem of statistically learning typical features which characterize object categories and particular features which characterize individual objects in the categories. For this purpose, we propose a probabilistic learning method of object categories and their composition based on a bag of feature representation of co-occurring segments of objects and their context. In this method, multi-class classifiers are learned based on intra-categorical probabilistic latent component analysis with variable number of classes and inter-categorical typicality analysis. Through experiments by using images of plural categories in an image database, it is shown that the method learns probabilistic structures which characterize not only object categories but also object composition of categories, especially typical and non-typical objects and context in each category.