Abstract: At present,the fuzzy segmentation algorithm of local features does not preprocess a single defocused image,resulting in low definition of the single defocused image and affecting the segmentation effect.The original fuzzy segmentation algorithm requires a large number of pixel labels in the process of pixel segmentation,and its segmentation process is complicated.Therefore,this paper proposed a method of using immune spectral clustering algorithm to excute fuzzy segmentation of the local features for a single defocused image .Firstly,the local fuzzy image is blurred again by using the method of block.Then,the variation of the singular value for the defocused image is compared,and the defocused image is identified based on this variation.Finally,the singular value features of a single defocused image are extracted,and the local features of a single defocused image are blurred.The spectral clustering method is used to cluster the pixels in the defocused image and the Nystrm approximation method is used to calculate the eigenvectors of the pixel similarity matrix,which reduces the computational complexity.The immune algorithm improves the accuracy of the clustering results and ensures the fuzzy segmentation results of the local features for defocused images.The experimental results show that the proposed algorithm can effectively segment the defocused image,the segmentation result is better and the calculation process is simpler.

Key words: Single defocused image,Local feature,Fuzzy segmentation,Immune spectrum clustering algorithm