Robust matching and recognition using context-dependent kernels

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 "contextdependent". 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 fixedpoint of this energy is a "context-dependent" kernel ("CDK") which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging ...

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, Renaud Keriven
• Keywords: ICML 2008, Kernel Methods, Machine Learning, Object Recognition, Pattern Recognition Problems