Object categorization with SVM: kernels for local features

Eichhorn, J; Chapelle, O

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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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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D8B5-C Version Permalink: https://hdl.handle.net/21.11116/0000-0006-C580-A

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MPIK-TR-137.pdf (Publisher version), 595KB

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Creators:
Eichhorn, J^{1, 2}, Author
Chapelle, O^{1, 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: Published in Print: 2004-07

Publication Status: Published in print

Pages: 9

Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics

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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: -