Abstract
A new algorithm for unsupervised textured image segmentation is presented. The image comprises M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vector valued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the expectation-maximization (EM) algorithm. An iterative algorithm is used with the underlying field modeled as a multilevel logistic Markov random field. The results obtained on two-region and four-region textured images are impressive, and the classification error is less than 3%. The algorithm is not limited to textured images but can also be applied to any vector-valued signals. >
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