Visual categorization based on learning contextual probabilistic latent component tree

Authors:
Masayasu Atsumi
Affiliations:
Dept. of Information Systems Science, Faculty of Engineering, Soka University, Hachioji, Tokyo, Japan
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

This paper describes a probabilistic learning method that is named a contextual probabilistic latent component tree for object and scene categorization. In this method, object classes are obtained by clustering a set of object segments extracted from scene images of each scene category and their categorical co-occurrence relations in scene categories are embedded in the probabilistic latent component tree that is generated as a classification tree of all the object classes of all the scene categories. Through experiments by using images of plural categories in an image database, it is shown that the co-occurrence relation of object categories in scene categories improves performance for object and scene recognition.