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  Object categorization with SVM: kernels for local features

Eichhorn, J., & Chapelle, O.(2004). Object categorization with SVM: kernels for local features (137). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-137.pdf (Publisher version), 595KB
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Eichhorn, J1, 2, Author              
Chapelle, O1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

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 Dates: 2004-07
 Publication Status: Published in print
 Pages: 9
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 137
BibTex Citekey: 2778
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 137 Sequence Number: - Start / End Page: - Identifier: -