Context-dependent kernel design for object matching and recognition

Abstract : The success of kernel methods including support vector networks (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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  • HAL Id : hal-00835081, version 1


Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven. Context-dependent kernel design for object matching and recognition. CVPR, Jun 2008, Anchorage, United States. pp.1-8. ⟨hal-00835081⟩



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