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  Recognition with Local Features: the Kernel Recipe

Wallraven, C., Caputo, B., & Graf, A. (2003). Recognition with Local Features: the Kernel Recipe. In Ninth IEEE International Conference on Computer Vision (pp. 257-264). Los Alamitos, CA, USA: IEEE Computer Society.

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Wallraven, C1, 2, Autor           
Caputo, B, Autor
Graf, ABA1, 2, Autor           
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Zusammenfassung: Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that this type of kernel is suitable for many standard local feature techniques in computer vision. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs using the proposed kernel perform better than standard Nearest-Neighbor techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.

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 Datum: 2003-10
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1109/ICCV.2003.1238351
BibTex Citekey: 2270
 Art des Abschluß: -

Veranstaltung

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Titel: Ninth IEEE International Conference on Computer Vision (ICCV 2003)
Veranstaltungsort: Nice, France
Start-/Enddatum: 2003-10-13 - 2003-10-16

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Titel: Ninth IEEE International Conference on Computer Vision
Genre der Quelle: Konferenzband
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Ort, Verlag, Ausgabe: Los Alamitos, CA, USA : IEEE Computer Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 257 - 264 Identifikator: ISBN: 0-7695-1950-4