Unsupervised Texture Segmentation

Segmentation is the fundamental process which partitions a data space into meaningful salient regions. Image segmentation essentially affects the overall performance of any automated image analysis system thus its quality is of the utmost importance. Image regions, homogeneous with respect to some usually textural or colour measure, which result from a segmentation algorithm are analysed in subsequent interpretation steps. Texturebased image segmentation is area of intense research activity in the past thirty years and many algorithms were published in consequence of all this effort, starting from simple thresholding methods up to the most sophisticated random field type methods. Unsupervised methods which do not assume any prior scene knowledge which can be learned to help segmentation process are obviously more challenging than the supervised ones. Segmentation methods are usually categorized (Reed et al., 1993) as region-based, boundary-based, or as a hybrid of the two. Different published methods are difficult to compare because of lack of a comprehensive analysis together with accessible experimental data, however available results indicate that the ill-defined texture segmentation problem is still far from being satisfactorily solved. The clustering approach resulted in agglomerative and divisive algorithms which were modified for image segmentation as region-based merge and split algorithms. Spatial interaction models and especially Markov random fieldbased models are increasingly popular for texture representation (Kashyap, 1986; Reed et al., 1993; Haindl, 1991), etc. Several researchers dealt with the difficult problem of unsupervised segmentation using these models see for example (Panjwani et al., 1995; Manjunath et al., 1991; Andrey et al., 1998; Haindl, 1999) or (Haindl et al., 2004, 2005, 2006a). In this chapter we assume constant illumination and viewing angles for all scene textures, or alternatively that the Lambert law holds for all scene surfaces. If this assumption cannot be assumed than all textures have to be treated either as Bidirectional Texture Functions (BTFs) or some illumination invariant features (Haindl et al., 2006b; Vacha et al., 2007) have too be used.