Based On Chaos Particle Swarm And Fuzzy Clustering Image Segmentation Algorithm Research

Image segmentation is an important pre-process of the image analysis, and it is also one of the most basic skills in the entire image processing projects. As the image itself is uncertain and complex, but the characteristics can be well expressed by the theory of fuzzy clustering. In recent years, many scholars effort to applying the fuzzy clustering theory to image segmentation which are proved better than the traditional method significantly.The fuzzy C means clustering algorithm(Fuzzy C Means clustering, FCM) is the most classic based on the objective function, the most perfect theory, and the most widely used algorithm. It transforms clustering problem into a constrained nonlinear programming problem, and by use of the inside and outside iterative manner to continuously update the fuzzy membership matrix and the cluster centers in order to gain the minimum of the clustering objective function. The FCM algorithm is applied to image segmentation can be clever to avoid the traditional algorithm of multi-branch problem, and as an unsupervised clustering analysis algorithm, it is not need to set the threshold manually, and suitable for practical application. Therefore, the FCM algorithm which used in the image segmentation has become a focus of research in the field of image processing projects, and it has some practical value.The paper focuses on the FCM algorithm of image segmentation and algorithm improving in-depth. The main work and innovation can be summarized as follows:(1) Summarize the current situation of the fuzzy clustering algorithm of the image segmentation, and introduce the theory and basic idea of the commonly used segmentation algorithm (threshold, edge detection, region-growing, the segmentation algorithm combined with the other specific theories).(2) The initial values of the FCM algorithm are very sensitive, and the algorithm relies heavily on the choice of the initial cluster centers. When the initial cluster centers deviate seriously from the global optimal cluster centers, or the algorithm become precocious by some particles halt in the iteration, it is likely to fall into the local minima. With the help of the chaotic motion of ergodicity, randomness and other characteristics, combined with the particle swarm optimization features, improved the individual quality by use of the chaotic initialization particle swarm, and take advantage of the chaos disturbance to avoid certain particles halting caused by the local minima in the iteration process, the fuzzy C means clustering algorithm based on the chaotic particle swarm (CPSO-FCM) which used in segmentation is proposed. The experiments show that the effect of the algorithm to image segmentation is greatly improved. And it also has the good real-time and robustness.(3) By comparing the characteristics between the RGB space and the HIS space of color images, the algorithm is applied to the color image segmentation by transforming the color space, it replaces the RGB color space by the HIS color space, and replaces of the Euclidean Distance which used in the FCM algorithm by the Characteristic Distance. The experiments show that the algorithm can obtain good segmentation results for color images.