\input zb-basic
\input zb-ioport
\iteman{io-port 06075906}
\itemau{Yang, Guan; Feng, Guo-Can; Luo, Zhi-Hong; Liu, Zhi-Yong}
\itemti{Texture analysis using Gaussian graphical models.}
\itemso{Int. J. Wavelets Multiresolut. Inf. Process. 10, No. 2, Paper No. 15, 13 p. (2012).}
\itemab
Summary: 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 $L_1$-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.
\itemrv{~}
\itemcc{}
\itemut{Gaussian graphical models; model selection; texture synthesis; texture classification}
\itemli{doi:10.1142/S0219691312500154}
\end