Multi-target fuzzy cluster image variance detecting method based on non-control-neighborhood immune algorithm

The invention discloses a multi-target fuzzy cluster image variance detecting method based on a non-control-neighborhood immune algorithm, and solves the problem that details and noises cannot be balanced by a conventional cluster algorithm. The realization method comprises the following steps: setting the iteration number and other operation parameters; randomly generating an initial population based on a center code; taking the similarity measure of the Euclidean distance among pixels as well as the similarity measure of the Euclidean distance among spatial points and neighborhoods thereof as the optimization targets; updating membership grade; updating superiority antibody groups according to the optimization targets; selecting the non-control-neighborhood; immunizing the antibody group, and performing circulation when necessary; judging whether the end condition is met or not, obtaining the cluster result through the membership grade if the end condition is met, and outputting the divided images. The mult