Semisupervised Approach to Surrogate-Assisted Multiobjective Kernel Intuitionistic Fuzzy Clustering Algorithm for Color Image Segmentation

As next generation of the web, the semantic web aims at a more intelligent web severing machines as well as people, based on radical notions of information sharing and acquisition. For color image segmentation, semantic color is our focus. One method of color partition is fuzzy clustering which has been widely used in image segmentation. However, the fuzzy clustering algorithm is parameter sensitive, and lack of availability because of its initial focus on physical features. To improve the above problems, a novel fuzzy clustering method based on semantic color retrieval for image segmentation is proposed in this paper. The method is realized by modifying the membership function in the conventional clustering algorithm and by constructing the semantic color retrieval mechanism to achieve the semantic color extraction, which take human visual subjectivity into account in semantics. Experimental results show that the presented method performs more effectively than the previous algorithm.

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A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

Journal of Intelligent Systems ◽

10.1515/jisys-2016-0241 ◽

2018 ◽

Vol 27 (4) ◽

pp. 593-607 ◽

Cited By ~ 9

Author(s):

S.V. Aruna Kumar ◽

B.S. Harish

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Medical Image ◽

Clustering Algorithm ◽

Intuitionistic Fuzzy Set ◽

Medical Image Segmentation ◽

Cluster Center ◽

Intuitionistic Fuzzy ◽

Elementary Condition ◽

Intuitionistic Fuzzy Clustering

Abstract This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

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Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

Journal of Applied Research and Technology ◽

10.22201/icat.24486736e.2021.19.2.1581 ◽

2021 ◽

Vol 19 (2) ◽

pp. 140-152

Author(s):

Dante Mújica Vargas

Keyword(s):

Fuzzy Sets ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Experimental Tests ◽

Color Images ◽

Intuitionistic Fuzzy ◽

Fuzzy Clustering Algorithm ◽

Natural Color ◽

Intuitionistic Fuzzy Clustering ◽

Segmentation Task

A scheme to develop the image over-segmentation task is introduced in this paper, it considers the pixels of an image as intuitive fuzzy sets and develops an intuitionistic clustering process of them. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. Experimental tests were developed considering biomedical grayscale and natural color images. The robustness and effectiveness of this proposal was verified by quantitative and qualitative results.

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