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Class Specific Object Recognition using Kernel Gibbs Distributions
Caputo, Barbara (Idiap Research Institute (Martigny, Suïssa))

Date: 2008
Abstract: Feature selection is crucial for effective object recognition. The subject has been vastly investigated in the literature, with approaches spanning from heuristic choices to statistical methods, to integration of multiple cues. For all these techniques the final result is a common feature representation for all the considered object classes. In this paper we take a completely different approach, using class specific features. Our method consists of a probabilistic classifier that allows us to use separate feature vectors, selected specifically for each class. We obtain this result by extending previous work on Class Specific Classifiers and Kernel Gibbs distributions. The resulting method, that we call Kernel-Class Specific Classifier, allows us to use a different kernel for each object class by learning it. We present experiments of increasing level of difficulty, showing the power of our approach.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Reconeixement d'objectes ; Visió Artificial ; Anàlisi estadística de patrons ; Reconocimiento de objetos ; Visión Artificial ; Análisis estadístico de patrones ; Object recognition ; Machine vision ; Statistical pattern analysis
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 7 n. 2 (2008) p. 96-109, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.221

14 p, 889.7 KB

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 Record created 2010-01-15, last modified 2020-11-06

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