Context-dependent kernel design for object matching and recognition - École des Ponts ParisTech Access content directly
Conference Papers Year : 2008

Context-dependent kernel design for object matching and recognition


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.
Fichier principal
Vignette du fichier
CVPR08c.pdf (462.69 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00835081 , version 1 (18-06-2013)


  • 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⟩
215 View
151 Download


Gmail Facebook Twitter LinkedIn More