Colour image segmentation using adaptive mean shift filters

A novel adaptive mean shift filter is proposed for unsupervised color image segmentation. Segmentation is obtained by iteratively estimating local clusters and modifying (filtering) pixels along the steepest ascent towards their nearest clusters using local data from randomly partitioned image, followed by a simple post processing. Several new steps are introduced to avoid pixels drifting towards shaded regions, pixel filtering using outliers and other local mode, and the blur of image structures. In each local area, the number of clusters is set to be adaptive according to the dynamic range of the local histogram. The filter is applied only to a radial set of pixels chosen from the color histogram, instead of all pixels in the local area. Further, the random partitions of the image make it possible for parallel processing of each local area, hence the potential of fast processing. Experiments were performed on color images with different complexity, and good segmentation results were obtained. Some preliminary evaluations were also performed using the uniformity measure.