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Conference Paper

Object Categorization via Local Kernels

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Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Caputo, B., Wallraven, C., & Nilsback, M.-E. (2004). Object Categorization via Local Kernels. In 17th International Conference on Pattern Recognition: ICPR 2004 (pp. 132-135). Los Alamitos, CA, USA: IEEE Computer Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-F384-4
Abstract
In this paper we consider the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer kernels. This class of kernels allows us to perform scalar products on feature vectors consisting of local descriptors, computed around interest points (like corners); these feature vectors are generally of different lengths for different images. The resulting framework is able to recognize multi-object categories in different settings, from lab-controlled to real-world scenes. We present several experiments, on different databases, and we benchmark our results with state-of-the-art algorithms for categorization, achieving excellent results.