A regularized simultaneous autoregressive model for texture classification

In this paper, we present a new method for texture classification which we call the regularized simultaneous autoregressive method (RSAR). The regularization technique is introduced. With the technique, the new algorithm RSAR outperforms the traditional algorithm in texture classification. Particularly, our new algorithm is useful for extracting texture from the image which is coarse or contains too much noise. where I is all the neighboring pixels of the pixel s , ( )sε is an independent Gaussian random variable with zero mean and variance . 2 σ ( ) Irr ∈,θ , are the model parameters characterizing the dependence of a pixel to its neighbors, and µ is the bias which is dependent on the mean gray value of the image. All parameters ,, σµ and ( )rθ can be estimated from a given window (sub image) by the least square estimation technique or the maximum likelihood estimation method. As we have noted that this kind of estimation is unstable. Other researchers have also made some re...

• Type: Conference
• Year: 2003
• Where: ISCAS
• Authors: Yao-wei Wang, Yan-fei Wang, Wen Gao, Yong Xue
• Keywords: Estimation, ISCAS 2003, Simultaneous Autoregressive Method, Texture Classification