Multimodal object recognition using random clustering trees

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/82801

dc.contributor	Institut de Robòtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
dc.contributor	Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.contributor.author	Villamizar Vergel, Michael Alejandro
dc.contributor.author	Garrell Zulueta, Anais
dc.contributor.author	Sanfeliu Cortés, Alberto
dc.contributor.author	Moreno-Noguer, Francesc
dc.date	2015
dc.identifier.citation	Villamizar, 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.
dc.identifier.citation	10.1007/978-3-319-19390-8_56
dc.identifier.uri	http://hdl.handle.net/2117/82801
dc.language.iso	eng
dc.publisher	Springer
dc.relation	http://link.springer.com/chapter/10.1007%2F978-3-319-19390-8_56
dc.relation	info:eu-repo/grantAgreement/EC/FP7/287617/EU/Aerial Robotics Cooperative Assembly System/ARCAS
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject	object recognition
dc.subject	random trees
dc.subject	clustering
dc.subject	boosting
dc.subject	Classificació INSPEC::Pattern recognition::Object recognition
dc.title	Multimodal object recognition using random clustering trees
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	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
dc.description.abstract	Peer Reviewed

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