Research On Image Segmentation Methods Based On Color Similarity Coefficient

Over the recent years, along with the improvement of processing ability of computer system and the increasing applications of color image, the color image segmentation, which can be seen as an expansion of the gray image segmentation has attracted more and more attentions. However, most methods for gray image segmentation can not be applied to color image directly, so it is necessary to modify them basing on the color information or to develop special methods for color image segmentation.The research of methods, which takes advantage of a clustering algorithm named DBSCAN and Cellular Automata (CA), for color image segmentation is showed in this paper. A measurement named color similarity coefficient is used to estimate the similarity between two kinds of color in our methods.Firstly, an improved method based on region growing for color image segmentation is presented. The density-based clustering algorithm DBSCAN is applied to the region growing rules. The results of our experiments demonstrate that the proposed method can efficiently segment color image and has an ability to resist noise.Secondly, a new method based on Cellular Automata for color image edge detection is proposed. Based on analyzing a Cellular Automata of predecessor, we establish and improve its corresponding model of population evolution. Finally, we build a new Cellular Automata with new evolution rules that are derived from the improved model. The results of our experiments demonstrate that the new-built Cellular Automata works better at detecting edge of color image than the predecessor's.