Learning visual object categories and their composition based on a probabilistic latent variable model

  • Authors:

  • Masayasu Atsumi

  • Affiliations:

  • Dept. of Information Systems Science, Faculty of Engineering, Soka University, Hachioji, Tokyo, Japan

  • Venue:
  • ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
  • Year:
  • 2010

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Abstract

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 cooccurring segments of objects and their context. In this method, multiclass 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.