Efficient image segmentation by mean shift clustering and MDL-guided region merging

Abstract

We present an efficient color and texture segmentation algorithm by combining two statistical techniques: mean shift clustering and minimum description length (MDL) principle. Mean shift clustering is proven in generating robust and accurate segmentation results for color images, but the selection of the two scale parameters remains a challenging problem for images with texture. Optimization based on MDL principle requires little parameter tuning, but the initial input has a strong impact on its efficiency and effectiveness. Our approach is to apply mean shift clustering to generate an initial over-segmentation and then merge regions based on MDL principle. Objects with texture can be extracted with reasonable accuracy by merging regions under the guidance of MDL principle, without the need of convolving the image with a bank of filters. Experimental results on a variety of natural scene images are reported and compared with the JSEG algorithm. It takes about 1 second for our algorithm to process a 320/spl times/240 color image on a conventional PC.