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Conference Papers Year : 2008

Robust matching and recognition using context-dependent kernels

Abstract

The success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as "context-dependent". Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criteria which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a "context-dependent" kernel ("CDK") which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with "context-free" kernels.
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Dates and versions

hal-00834980 , version 1 (17-06-2013)

Identifiers

  • HAL Id : hal-00834980 , version 1

Cite

Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven. Robust matching and recognition using context-dependent kernels. ICML, Jul 2008, Helsinki, Finland. pp.856-863. ⟨hal-00834980⟩
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