Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

Ioana Ilea; Lionel Bombrun; Salem Said; Yannick Berthoumieu

Conference papers

Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

Ioana Ilea ^{1, 2} Lionel Bombrun ² Salem Said ² Yannick Berthoumieu ²

1 UTCN - Universitatea Tehnica din Cluj-Napoca

2 IMS - Laboratoire de l'intégration, du matériau au système

Abstract : This paper introduces a novel local model for the classification of covariance matrices: the co-occurrence matrix of covariance matrices. Contrary to state-of-the-art models (BoRW, R-VLAD and RFV), this 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.

Keywords : Co-occurrence matrix Riemannian Gaussian distributions classification covariance matrix

Document type :

Conference papers

Domain :

Computer Science [cs] / Signal and Image Processing

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Submitted on : Monday, November 6, 2017 - 10:12:56 AM
Last modification on : Thursday, February 7, 2019 - 4:48:03 PM

Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

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Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images

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