Object Categorization via Local Kernels

Authors:
Barbara Caputo;Christian Wallraven;Maria-Elena Nilsback
Affiliations:
NADA/CVAP, KTH, Stockholm, Sweden;MPI for Biological Cybernetics, Germany;NADA/CVAP, KTH, Stockholm, Sweden
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Year:
2004

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Abstract

This paper considers 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.