Abstract
This paper presents an unsupervised fuzzy clustering based on evolutionary algorithm for image segmentation. It needs no prior information about exact numbers of segments. Local and nonlocal spatial information derived from observed images are incorporated into fuzzy clustering process. It consists of three major procedures. First, a multi-objective evolutionary sampling is proposed to locate image pixels with a variety of image information. Secondly, optimizing fuzzy compactness and fuzzy separation, a multi-objective evolutionary fuzzy clustering with spatial information is performed on sampling pixels. The particular numbers of segments and balances of spatial information can be obtained. Then fuzzy clustering segmentation on whole image is carried out by two fuzzy clustering approaches, which are depended on fuzzy c-means and evolutionary algorithm respectively. To enhance qualities of final segmentation results, a label correction based on entropy and local spatial information is introduced. Experiments on different types of images demonstrate the effectiveness of our approach for image segmentation.
Highlights
Image segmentation, the extraction of important objects from observed image, has received extensive attention since past few decades [1]
This paper presents an unsupervised evolutionary algorithm (EA)-based fuzzy clustering to deal with image segmentation without prior information about exact number of segments
A multi-objective evolutionary fuzzy clustering with local and nonlocal spatial information is proposed to perform on sampling pixels
Summary
The extraction of important objects from observed image, has received extensive attention since past few decades [1]. It is an essential step in many image applications. Among the widely applied techniques for image segmentation, clustering [4]–[6] aims to divide a dataset into a finite set of clusters. It partitions image pixels into several clusters according to some characteristics, such as. Incorporating fuzzy set [7] into clustering [8], fuzzy clustering has been studied and utilized in many substantive applications [9]–[12]
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