Optimization of the Hölder Image Descriptor using a Genetic Algorithm

Abstract : Local image features can provide the basis for robust and invariant recognition of objects and scenes. Therefore, compact and distinctive representations of local shape and appearance has become invaluable in modern computer vision. In this work,we study a local descriptor based on the Hölder exponent, a measure of signal regularity. The proposal is to find an optimal number of dimensions for the descriptor using a genetic algorithm (GA). To guide the GA search, fitness is computed based on the performance of the descriptor when applied to standard region matching problems. This criterion is quantified using the F-Measure, derived from recall and precision analysis. Results show that it is possible to reduce the size of the canonical Hölder descriptor without degrading the quality of its performance. In fact, the best descriptor found through the GA search is nearly 70% smaller and achieves similar performance on standard tests.
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Communication dans un congrès
GECCO 2010. Best paper award in "Real world applications"., Jul 2010, Portland Oregon, United States. ISBN 978-1-4503-0072-8, pp.1147-1154, 2010
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Contributeur : Pierrick Legrand <>
Soumis le : lundi 28 novembre 2011 - 17:11:08
Dernière modification le : jeudi 11 janvier 2018 - 06:22:36
Document(s) archivé(s) le : mercredi 29 février 2012 - 02:20:19

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  • HAL Id : inria-00534457, version 1

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Leonardo Trujillo, Pierrick Legrand, Gustavo Olague, Cynthia Pérez. Optimization of the Hölder Image Descriptor using a Genetic Algorithm. GECCO 2010. Best paper award in "Real world applications"., Jul 2010, Portland Oregon, United States. ISBN 978-1-4503-0072-8, pp.1147-1154, 2010. 〈inria-00534457〉

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