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Context-Dependent Kernels for Object Classification

Hichem Sahbi 1 Jean-Yves Audibert 2, 3, 4 Renaud Keriven 2, 4
2 imagine [Marne-la-Vallée]
CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech, LIGM - Laboratoire d'Informatique Gaspard-Monge
3 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Kernels are functions designed in order to capture resemblance between data and they are used in a wide range of machine learning techniques, including support vector machines (SVMs). In their standard version, commonly used kernels such as the Gaussian one show reasonably good performance in many classification and recognition tasks in computer vision, bioinformatics, and text processing. In the particular task of object recognition, the main deficiency of standard kernels such as the convolution one resides in the lack in capturing the right geometric structure of objects while also being invariant. We focus in this paper on object recognition using a new type of kernel referred to as "context dependent." Objects, seen as constellations of interest points, are matched by minimizing an energy function mixing 1) a fidelity term which measures the quality of feature matching, 2) a neighborhood criterion 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 which is also positive definite. Experiments conducted on object recognition show that when plugging our kernel into SVMs, we clearly outperform SVMs with context-free kernels.
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Soumis le : mercredi 21 décembre 2011 - 16:28:40
Dernière modification le : jeudi 1 juillet 2021 - 17:58:06
Archivage à long terme le : : jeudi 22 mars 2012 - 02:28:54


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Hichem Sahbi, Jean-Yves Audibert, Renaud Keriven. Context-Dependent Kernels for Object Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2011, 33 (4), pp.699 - 708. ⟨10.1109/TPAMI.2010.198⟩. ⟨hal-00654341⟩