基于互补空间信息的多目标进化聚类图像分割  

Multi-objective Evolutionary Clustering with Complementary Spatial Information for Image Segmentation

作　　者：赵凤[1] 刘汉强[2] 范九伦[1] 

机构地区：[1]西安邮电大学通信与信息工程学院,西安710061 [2]陕西师范大学计算机科学学院,西安710062

出　　处：《电子与信息学报》2015年第3期672-678,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61102095;61202153;61340040);陕西省科技计划(2014KJXX-72);陕西省自然科学基础研究计划(2012JQ8045;2014JQ8336;2014JM8307;2013JM3081)资助课题

摘　　要：现有的多目标进化聚类算法应用于图像分割时,没有考虑图像的任何空间信息,使得该类算法在含噪图像上的分割性能不理想。该文鉴于图像的局部空间信息和非局部空间信息的互补性,试图将这两种空间信息同时引入到聚类有效性函数中,构造了融合互补空间信息的目标函数,进而提出了应用于图像分割的基于互补空间信息的多目标进化聚类算法。该算法采用染色体可变长编码策略在进化过程中自动确定图像分割数目,减少了人为干预。自然图像的分割实验表明,该算法不但能在含噪图像上取得较为满意的分割性能,而且适用于多种类型的含噪图像。When existing multi-objective evolutionary clustering algorithms is applied to image segmentation, it can not obtain satisfactory segmentation performance on an image corrupted by noise due to no consideration of any spatial information derived from the image. Based on the complementarity of the local spatial information and the non local spatial information of the image, these two kinds of spatial information are introduced into a cluster validity function, and a novel objective function with complementary spatial information is constructed, and then a multi-objective evolutionary clustering algorithm with complementary spatial information for image segmentation is proposed. In order to reduce human intervention, the variable string length real coded technique is adopted to determine automatically the number of clusters during the evolving process. Natural image segmentation experiments show that the proposed method not only can obtain satisfactory segmentation performance on noisy images, but also can be suitable for many types of noisy images.

关 键 词：图像分割 多目标进化聚类 互补空间信息 局部空间信息 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]