CAJ | 학술논문

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。
현유적다목표진화취류산법응용우도상분할시，몰유고필도상적임하공간신식，사득해류산법재함조도상상적분할성능불이상。해문감우도상적국부공간신식화비국부공간신식적호보성，시도장저량충공간신식동시인입도취류유효성함수중，구조료융합호보공간신식적목표함수，진이제출료응용우도상분할적기우호보공간신식적다목표진화취류산법。해산법채용염색체가변장편마책략재진화과정중자동학정도상분할수목，감소료인위간예。자연도상적분할실험표명，해산법불단능재함조도상상취득교위만의적분할성능，이차괄용우다충류형적함조도상。
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.