Multi Dimension Fuzzy C-means Color Image Segmentation Based on Intelligent Analysis Algorithm

In our daily life, people often encounter some fuzzy problems, such as image segmentation, digital filtering, etc. Therefore, this paper studies the color scene information compression based on intelligent analysis algorithm. First, the combination of fuzzy clustering method and gray model hair preprocessing method is introduced. Then, the simulation experiment results of Matlab software prove that the average number of the intelligent analysis algorithm is slightly larger in terms of the average number of the multi-dimensional FCCI(FCCI) segmentation technique, indicating that the performance of the intelligent analysis algorithm has been improved, mainly because the algorithm uses the midpoint method to select the original initial clustering center in the early stage, The algorithm can effectively reduce the number of iterations in the iterative process, thus improving the performance of the algorithm. This also shows that the method can well suppress noise and improve the effect of edge region separation. Finally, an idea of combining color threshold to effectively extract image segmentation features is proposed, and to some extent, adding color threshold to the image provides a theoretical basis and practical guidance for its further promotion.