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Título:
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Random clustering ferns for multimodal object recognition
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Autor/a:
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Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc
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Otros autores:
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Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
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Abstract:
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The final publication is available at link.springer.com |
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Abstract:
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We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control
-Recognition
-Random trees
-pLSA
-Boosting
-Classificació INSPEC::Pattern recognition |
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Derechos:
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Tipo de documento:
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Artículo - Versión presentada
Artículo |
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