A novel technique for unsupervised texture segmentation

Image texture segmentation is an important problem and occurs frequently in many image processing applications. Although, a number of algorithms exist in the literature. Methods that rely on the use of expectation-maximisation algorithm are gaining a growing interest. The main feature of this algorithm is that it is capable of estimating the parameters of mixture distribution. This paper presents a novel unsupervised algorithm based on expectation-maximisation algorithm where the analysis is applied on vector data rather than the grey level. This is achieved by defining a likelihood function which measures how the estimated features are fitting the present data. Experimental results on images containing various synthetic and natural textures have been carried out and a comparison with existing and similar techniques has shown the superiority of the proposed method