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TEXTURE ANALYSIS USING GAUSSIAN GRAPHICAL MODELS.
- Source :
- International Journal of Wavelets, Multiresolution & Information Processing; Mar2012, Vol. 10 Issue 2, p1250015-1-1250015-13, 13p, 3 Black and White Photographs, 3 Charts, 1 Graph
- Publication Year :
- 2012
-
Abstract
- Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L<subscript>1</subscript>-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02196913
- Volume :
- 10
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- International Journal of Wavelets, Multiresolution & Information Processing
- Publication Type :
- Academic Journal
- Accession number :
- 74220048
- Full Text :
- https://doi.org/10.1142/S0219691312500154