Multimodal Object Recognition Using Random Clustering Trees

In this paper, we present an object recognition approach that in addition allows to discover intra-class modalities exhibiting high-correlated 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.

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