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<publisher>HAL CCSD</publisher>
<title lang=en>k-NN Boosting Prototype Learning for Object Classification</title>
<creator>Piro, Paolo</creator>
<creator>Barlaud, Michel</creator>
<creator>Nock, Richard</creator>
<creator>Nielsen, Frank</creator>
<contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe IMAGES-CREATIVE ; Signal, Images et Systèmes (SIS) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX) ; Centre National de la Recherche Scientifique (CNRS) - Polytechnique - X</contributor>
<source>International Workshop on Image Analysis for Multimedia Interactive Services</source>
<source>WIAMIS 2010 - 11th Workshop on Image Analysis for Multimedia Interactive Services</source>
<coverage>Desenzano del Garda, Italy</coverage>
<publisher>IEEE</publisher>
<identifier>hal-00481725</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00481725</identifier>
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<source>https://hal.archives-ouvertes.fr/hal-00481725</source>
<source>WIAMIS 2010 - 11th Workshop on Image Analysis for Multimedia Interactive Services, Apr 2010, Desenzano del Garda, Italy. IEEE, pp.1-4, 2010</source>
<language>en</language>
<subject>[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Object classification is a challenging task in computer vision. Many approaches have been proposed to extract meaningful descriptors from images and classifying them in a supervised learning framework. In this paper, we revisit the classic k-nearest neighbors (k-NN) classification rule, which has shown to be very effective when dealing with local image descriptors. However, k-NN still features some major drawbacks, mainly due to the uniform voting among the nearest prototypes in the feature space. In this paper, we propose a generalization of the classic k-NN rule in a supervised learning (boosting) framework. Namely, we redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. To induce this classifier, we propose a novel learning algorithm, MLNN (Multiclass Leveraged Nearest Neighbors), which gives a simple procedure for performing prototype selection very efficiently. We tested our method on 12 categories of objects, and observed significant improvement over classic k-NN in terms of classification performances.</description>
<date>2010-04-12</date>
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