Optimization of the Hölder Image Descriptor using a Genetic Algorithm
hal.structure.identifier | Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana] | |
dc.contributor.author | TRUJILLO, Leonardo | |
hal.structure.identifier | Advanced Learning Evolutionary Algorithms [ALEA] | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | LEGRAND, Pierrick | |
hal.structure.identifier | Centro de Investigacion Cientifica y de Education Superior de Ensenada [Mexico] [CICESE] | |
dc.contributor.author | OLAGUE, Gustavo | |
hal.structure.identifier | Centro de Investigacion Cientifica y de Education Superior de Ensenada [Mexico] [CICESE] | |
dc.contributor.author | PÉREZ, Cynthia | |
dc.date.issued | 2010 | |
dc.date.conference | 2010-07-07 | |
dc.description.abstractEn | 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. | |
dc.language.iso | en | |
dc.title.en | Optimization of the Hölder Image Descriptor using a Genetic Algorithm | |
dc.type | Communication dans un congrès | |
dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
dc.subject.hal | Informatique [cs]/Apprentissage [cs.LG] | |
dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
bordeaux.page | 1147-1154 | |
bordeaux.volume | ISBN 978-1-4503-0072-8 | |
bordeaux.conference.title | GECCO 2010. Best paper award in "Real world applications". | |
bordeaux.country | US | |
bordeaux.conference.city | Portland Oregon | |
bordeaux.peerReviewed | oui | |
hal.identifier | inria-00534457 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | oui | |
hal.conference.end | 2010-07-11 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//inria-00534457v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2010&rft.volume=ISBN%20978-1-4503-0072-8&rft.spage=1147-1154&rft.epage=1147-1154&rft.au=TRUJILLO,%20Leonardo&LEGRAND,%20Pierrick&OLAGUE,%20Gustavo&P%C3%89REZ,%20Cynthia&rft.genre=unknown |
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