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.

Object categorization via local kernels / Caputo, Barbara; Wallraven, C; Nilsback, M. E.. - STAMPA. - 2(2004), pp. 132-135. ((Intervento presentato al convegno 17th International Conference on Pattern Recognition tenutosi a Cambridge; UK nel 23-26 August 2004.

Object categorization via local kernels

CAPUTO, BARBARA;
2004

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/950640
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