A Novel Intuitionistic Fuzzy Clustering Algorithm Based on Feature Selection for Multiple Object Tracking

2019 ◽  
Vol 21 (5) ◽  
pp. 1613-1628 ◽  
Author(s):  
Liang-qun Li ◽  
Xiao-li Wang ◽  
Zong-xiang Liu ◽  
Wei-xin Xie
Keyword(s):  
Feature Selection ◽  
Object Tracking ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Multiple Object Tracking ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Multiple Object ◽  
Selection For ◽  
Intuitionistic Fuzzy Clustering

scholarly journals Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

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.

Cuckoo-search based Intuitionistic Fuzzy Clustering Algorithm

2017 ◽  
Vol 7 (2) ◽  
pp. 289 ◽  
Author(s):  
S. Parvathavarthini ◽  
N. Karthikeyani ◽  
Visalakshi S. Shanthi ◽  
J. Madhan Mohan
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Cuckoo Search ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Intuitionistic Fuzzy Clustering

scholarly journals Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection

2021 ◽  
Vol 1948 (1) ◽  
pp. 012033
Author(s):  
Wei Cai ◽  
Shengbing Xu ◽  
LiangJun Zhang ◽  
Jiongzhi Liu ◽  
Peixuan Chen
Keyword(s):  
Feature Selection ◽  
Manifold Learning ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Cross Entropy ◽  
Pairwise Constraints ◽  
Fuzzy Clustering Algorithm

Classification of Ship’ Magnetic Field and Feature Selection Based on the Improved Weighted Fuzzy Clustering Algorithm

2013 ◽  
Vol 401-403 ◽  
pp. 1353-1357
Author(s):  
Wu Di Wen ◽  
Zhong Le Liu ◽  
Zhi Qiang Zhang
Keyword(s):  
Magnetic Field ◽  
Feature Selection ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Field Data ◽  
Clustering Algorithm ◽  
Magnetic Field Data ◽  
Fuzzy Clustering Algorithm ◽  
Improved Algorithm

Magnetic field data of ship has three-component,and traditional weighted fuzzy clustering algorithm(FCA) can’t deal with the three-component data. We improve the traditional FCA by changing the objective function and added weights calculation of three-component of magnetic field in the function.Give the equation to compute the weights of three-component.Put forward new steps for improved algorithm.Use ships’ data to test the improved algorithm and giving the conclusion.

scholarly journals A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

2018 ◽  
Vol 27 (4) ◽  
pp. 593-607 ◽  
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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