A fast clustering algorithm in image segmentation

Image segmentation is the basis of image analysis & understanding, and a crucial part and an oldest and hardest problem of image processing. Clustering, a vital element of model identification field means distinguishing and classifying things that are provided with similar properties. As it so happens that the problem of image segmentation is exactly the problem of classifying pixel set of image, clustering analysis is naturally applied into image segmentation. Based on image segmentation and model identification technologies and considering application characteristics of clustering method into image segmentation, this paper proposes a fuzzy clustering application into image segmentation.

I. Introduction

Image segmentation has been as widely applied as in almost every field that is related to image processing and involved in miscellaneous image such as remote sensing image, medical image and traffic image etc. Due to the important role that image segmentation technology has been playing in image processing, researches on image segmentation algorithm have long been in existence ever since the twenties of the last century, of which those really early and classical image segmentation algorithms including threshold-selection-based image segmentation algorithm, area-based image segmentation algorithm and edge-detection-based image segmentation algorithm etc. are still popularly in use. While problems with these traditional algorithms are that they are either confronting with feature extraction problem or disagreeing with a featured condition that there're uncertainty and fuzziness with an image [8]. In order to solve these problems fundamentally so as to achieve a more extensive applicable scope of the image segmentation algorithms, fuzzy clustering algorithm is introduced into image segmentation as a hotspot of modern image processing technology.