Pixel clustering for color image segmentation

Abstract

Image segmentation using a hierarchical sequence of piecewise constant approximations that minimally differ from the original image in terms of the total squared error is discussed. It is proposed to obtain these approximations by two combined clustering and segmentation methods based on clustering image pixels using Ward's method. In the first method, the number of segments in clusters is reduced in the course of hierarchical clustering by reclassifying pixels from one cluster to another. In the second method, a limited number of superpixels representing connected segments of the image are formed by enlarging source pixels, and then the superpixels are clusterized by Ward's method. To decompose the image into superpixels, the segmentation quality is improved while preserving the number of segments. As a result, a noticeable improvement in the quality of image approximations is achieved, and their invariant encoding gives a marking of the image for subsequent object detection.