Abstract
This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. The segmented clusters are then restructured in order to isolate core clusters, i.e., patches in which the pixels are definitely associated with the same region. The image pixels representing the core clusters are used to form 3D color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution.
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