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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-10-24

AUTHORS

Ioana Ilea , Lionel Bombrun , Salem Said , Yannick Berthoumieu

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. More... »

PAGES

736-744

References to SciGraph publications

Book

TITLE

Geometric Science of Information

ISBN

978-3-319-68444-4
978-3-319-68445-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-68445-1_85

DOI

http://dx.doi.org/10.1007/978-3-319-68445-1_85

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092381071


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