Region-growing approach to colour segmentation using 3-D clustering and relaxation labelling

Abstract

The paper presents a new segmentation algorithm for colour images based on a series of region growing and merging processes. This algorithm starts with the region growing process, which groups pixels into homogeneous regions by combining the 3-D clustering and relaxation labelling techniques. Each resulting small region is then merged to the region which is the nearest to it in terms of colour similarity and spatial proximity. One problem with region growing is its inherent dependency on the selection of seed region, which can be avoided by using the relaxation labelling technique. Experimental results are presented to demonstrate the performance of the new method in terms of better segmentation and less sensitivity to noise, and in terms of computational efficiency. The segmentation results using the fuzzy c-means technique, the competitive learning neural network and a region growing and merging algorithm are also presented for comparison purposes.