Combining Local Recognition Methods for Better Image Recognition

Bart Lamiroy 1 Patrick Gros 2 Sylvaine Picard 3
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper we propose a comprehensive framework that allows existing local appearance methods to collaborate in order to overcome their mutual drawbacks. Our approach tends to use the best suited local descriptors for a recognition task, and is capable of combining evidence of different methods in the case where no clearly superior type of descriptor exists. We achieve this collaboration by locally matching geometric configurations and let each match contribute to the computation of the apparent motion between a model image and the unknown query image. We show in this paper that, if we have a set of local methods conforming to a small set of conditions, they can share information about evidence of objects in a scene. This shared evidence results in recognition performances that lie beyond the capacities of any of the currently used individual methods.
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Bart Lamiroy, Patrick Gros, Sylvaine Picard. Combining Local Recognition Methods for Better Image Recognition. Vision, BMVA, 2000, 17, pp.1-6. ⟨http://www.bmva.org/bmvc/2000/papers/p74.pdf⟩. ⟨inria-00604483⟩

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