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La matrice de co-occurrence des matrices de covariance : un nouveau modèle de codage pour la classification de textures

Abstract : This paper introduces a novel coding model for the classification from covariance matrice set: the co-occurrence matrix associated to a dictionary of covariance matrices. Contrary to state-of-the-art coding models (BoRW, R-VLAD and RFV), such local model exploits the spatial distribution of the patches. Starting from the generative mixture model of Riemannian Gaussian distributions, we introduce this local model. An experiment on texture image classification is then conducted on the VisTex and Outex_TC000_13 databases to evaluate its potential.
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https://hal.archives-ouvertes.fr/hal-01629143
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Submitted on : Monday, November 6, 2017 - 10:07:03 AM
Last modification on : Thursday, February 7, 2019 - 4:48:02 PM
Long-term archiving on: : Wednesday, February 7, 2018 - 1:00:26 PM

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  • HAL Id : hal-01629143, version 1

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Ioana Ilea, Lionel Bombrun, Salem Said, Yannick Berthoumieu. La matrice de co-occurrence des matrices de covariance : un nouveau modèle de codage pour la classification de textures. GRETSI, Sep 2017, Juan les Pins, France. ⟨hal-01629143⟩

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