关键词: 直觉模糊, 图像分割, 迭代自组织数据分析技术算法（ISODATA）

Abstract: ISODATA algorithm is capable of splitting and merging categories automatically. However, this kind of hard clustering fails to take into consideration the characteristics of image itself and human visual features. So its effect is generally not as good as that of fuzzy clustering algorithm. For most fuzzy recognition methods, if they are to be applied, the number of categories must be set beforehand. Besides, there is inherent defect in traditional fuzzy algorithms. By contrast, intuitionistic fuzzy is a kind of improvement to make up for the deficiencies of traditional fuzzy theory. Based on the advantages of ISODATA algorithm and intuitionistic fuzzy, with those critical functions which are related to membership and non-membership functions used as the measurement for clustering, this paper proposes a kind of ISODATA algorithm that is based on intuitionistic fuzzy, and introduces membership function that has been improved for practical purposes. This kind of function takes region as the sample to be classified. This paper verifies the effectiveness of the proposed algorithm by applying it to image segmentation.

Key words: intuitionistic fuzzy, image segmentation, Iterative Self-Organizing Data Analysis Technique Algorithm（ISODATA）