Name
Title | ||
|---|---|---|
Co-Occurrence Matrix Of Covariance Matrices: A Novel Coding Model For The Classification Of Texture Images |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-68445-1_85 | GEOMETRIC SCIENCE OF INFORMATION, GSI 2017 |
Keywords | DocType | Volume |
Co-occurrence matrix, Riemannian Gaussian distributions, Classification, Covariance matrix | Conference | 10589 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioana Ilea | 1 | 9 | 2.91 |
| Lionel Bombrun | 2 | 150 | 20.59 |
| Salem Said | 3 | 59 | 12.54 |
| Y. Berthoumieu | 4 | 389 | 51.66 |