关键词: 色彩分割, Lab均匀色彩空间, 模糊Histon直方图阈值, 模糊C均值（FCM）聚类

Abstract: This paper proposes a simple and effective adaptive unsupervised method. Fuzzy Histon threshold technique is applied to obtain all possible homogeneous regions of the color image in the Lab space. Finding the peak value, regions initialization, color on the regional similarity combination, it obtains the homogeneous regions labeling by the corresponding cluster centers. An adaptive FCM clustering algorithm is used to improve the compactness between the homogeneous regions in the final color segmentation. This algorithm has been successfully applied to the Berkeley image database, compared with some current segmentation algorithms, such as： Mean-Shift and NCuts make a reasonable division of better, in the vision it effectively extracts the target, has certain robustness.

Key words: color segmentation, Lab even color spaces, fuzzy Histon-histogram threshold, Fuzzy C-Means（FCM） clustering