Kernelizing Spatially Consistent Visual Matches for Fine-Grained Classification

Abstract : This paper introduces a new image representation relying on the spatial pooling of geometrically consistent visual matches. We therefore introduce a new match kernel based on the in- verse rank of the shared nearest neighbors combined with local geometric constraints. To avoid over tting and reduce processing costs, the dimensionality of the resulting over- complete representation is further reduced by hierarchically pooling the raw consistent matches according to their spa- tial position in the training images. The nal image repre- sentation is obtained by concatenating the resulting feature vectors at several resolutions. Learning from these represen- tations using a logistic regression classi er is shown to pro- vide excellent ne-grained classi cation performances out- performing the results reported in the literature on several classi cation tasks.
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https://hal.inria.fr/hal-01145988
Contributor : Alexis Joly <>
Submitted on : Monday, April 27, 2015 - 2:19:42 PM
Last modification on : Thursday, October 10, 2019 - 11:46:03 AM

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Valentin Leveau, Alexis Joly, Olivier Buisson, Patrick Valduriez. Kernelizing Spatially Consistent Visual Matches for Fine-Grained Classification. ICMR: International Conference on Multimedia Retrieval, Jun 2015, Shangai, China. pp.155-162, ⟨10.1145/2671188.2749328⟩. ⟨hal-01145988⟩

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