TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH

Abstract

The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed. The texture model is based on sets of gray level cooccurence (GLC) matrices rather than measures extracted from them. The algorithmic constituents for the segmentation scheme: choice of seed regions, normalized match distances between texture models, region homogeneity, and aggregation criteria are systematically developed. The unsupervised algorithm works so that “seed” regions are discovered by an image search process. Initial estimates of the texture model prototypes are automatically computed for each “seed” region, and classification thresholds are based on the variance of the model over the “seed” region. An aggregation process then results in regions being successively classified and segmented “out” of the image. This recursive process of segmentation is continued until all pixels are classified. The segmentation strategy was tested successfully on natural texture mosaics. The results are analytically presented. These experiments demonstrate that the unsupervised process can correctly identify the perceptual constituents of the image based on texture.