A New Segmentation Approach Based on Fuzzy Graph-Theory Clustering

Aiming at the limitation of traditional graph-theory clustering method in the process of image segmentation, a new segmentation approach is proposed, which uses fuzzy similarity relationship to weight the edges while a complete graph is constituted. And fuzzy maximum spanning tree is used to clustering. Thus the traditional graph-theory clustering method is improved as the fuzzy graph-theory clustering method. Use the local mean and local variance to construct bivector, define the pixel´s local mean and variance vector., then get the fuzzy similarity relationship of each pixel in the picture sequence. Experiments are conducted on two real pictures by MATLAB. Results show that different effects can be get by changing the parameter. And the flexibility is better than other contrast methods´.