Multimodal object recognition using random clustering trees

Abstract

In this paper, we present an object recognition approach that in addition allows to discover intra-class modalities exhibiting highcorrelated visual information. Unlike to more conventional approaches based on computing multiple specialized classifiers, the proposed approach combines a single classifier, Boosted Random Ferns (BRFs), with probabilistic Latent Semantic Analysis (pLSA) in order to recognize an object class and to find automatically the most prominent intra-class appearance modalities (clusters) through tree-structured visual words. The proposed approach has been validated in synthetic and real experiments where we show that the method is able to recognize objects with multiple appearances.