In this paper, we present an object recognition approach that in addition allows to discover intra-class mo dalities exhibiting high-correlated visual information. Unlike to more conventional approaches based on computing multiple sp ecialized classiers, the proposed approach combines a single classier, 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 mo dalities (clusters) through tree-structured visual words. The proposed approach has b een validated in synthetic and real experiments where we show that the method is able to recognize objects with multiple appearance
CitationVillamizar, M.A., Garrell, A., Sanfeliu, A., Moreno-Noguer, F. Multimodal object recognition using random clustering trees. A: Iberian conference on pattern recognition and image analysis. "7th Iberian Conference on Pattern Recognition and Image Analysis, 2015, Santiago de Compostela, in Pattern Recognition and Image Analysis, Vol 9117 of Lecture Notes in Computer Science". Santiago de Compostela: Springer, 2015, p. 496-504.
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