Scale space segmentation of color images using watersheds and fuzzy region merging

A multi-resolution segmentation approach for color images is proposed. The scale space is generated using the Perona-Malik diffusion approach and the watershed algorithm is employed to produce the regions in each scale. The dynamics of contours and the relative entropy of color regions distribution are estimated as region dissimilarity features across the scale-space stack, and combined using a fuzzy rule based system. A minima-linking process by downward projection is carried out and subsequently the region dissimilarity, combining color, scale and homogeneity is estimated for the finer scale (localization scale). The final segmentation is derived using a previously presented merging process. To validate its performance qualitative and quantitative results are provided.