IMAGE SEGMENTATION BASED ON MODEL SELECTION
Clustering-based image segment approach is popular in image processing. It consists in separating pixel features into clusters representing homogeneous regions. In the kind of methods, determining the number of clusters is an open problem. In this paper, we propose an efficient model selection algorithm for automatically determining the number of clusters. The algorithm roots the try-and-error approach. Due to the previous results to be used for initializing the next trail procedure, the FCM converge fast. So, the proposed algorithm is more efficient in term of computational time. Experimental results confirm efficiency of the proposed algorithm.
color image segmentation model selection try-and-error process Fuzzy C-Means clustering.
HAO-JUN SUN
College of Mathematics and Computer Science, Hebei University, Baoding 071002 China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1394-1399
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）