Visual categorization based on learning contextual probabilistic latent component tree

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. © 2012 Springer-Verlag.