Color image segmentation algorithm based on edgeflow and region merging

In order to overcome the over segmentation phenomenon of edgeflow-driven anistropic diffusion (EAD) and the high computational complexity of (MST) minimum spanning tree, a color image segmentation algorithm based on edgeflow and region merging is presented. First of all, the EAD is applied to the image to get a preliminary result. Then the MST is used to perform the globally optimized region merging. Finally, segmentation results are achieved after proper post-process. Because the MST is based on segmented regions instead of image pixels, this algorithm requires much lower computational complexity. In addition, the EAD focuses on local details while the MST captures global property, so the proposed algorithm combines both advantages. Experimental results clearly indicate that the approach we propose can not only get satisfactory segmentation results, but also decrease the runtime of the process.