Unsupervised EA-Based Fuzzy Clustering for Image Segmentation

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 multiobjective 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.

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

“…In Ref. [42], an unsupervised fuzzy clustering based on NSGAII for image segmentation was proposed, where local and nonlocal spatial information derived from observed images were incorporated into the clustering process. Zhong et al [43] proposed a multiobjective adaptive differential evolution for fuzzy clustering of remote sensing images by optimizing two cluster indexes simultaneously.…”

An application analysis method based on Clustering

“…Clustering method plays a vital role in data analysis, data mining, engineering signal processing, and so on [6,7]. The applications of clustering algorithm range from image processing [8], pattern recognition [9,10], recommendation [11], and others. Generally speaking, clustering is to partition the data into diverseclasses or groups according to a similarity measurement.…”

An Efficient Segmentation Technique for Different Medical Image Modalities

“…The FCM is noise sensitive, because the distribution of the image data is affected by the noise, which leads to two problems. The first is that the output image obtained by FCM algorithm is poor for segmentation; the other is that the number of FCM iterations is larger for an image corrupted by noise [1,4]. By introducing local spatial information to the FCM algorithm, it gets immunity to noise and shows superior performance for the segmentation process.…”

“…A structuring element is moved over the whole image [1]. Image segmentation [2,3,4] is the process of splitting an image into a number of non-overlapping segments (sets of pixels, also known as image objects). The success of the image analysis process depends on the accuracy of segmentation process, but a successful segmentation of an image is generally a difficult problem.…”