Hybrid method of spatial credibilistic clustering and particle swarm optimization: discussion and application

Authors:
Peihan Wen;Jian Zhou;Li Zheng;Xuan Chen;Brian Anderson
Affiliations:
Department of Industrial Engineering, Tsinghua University, Beijing, China;Department of Industrial Engineering, Tsinghua University, Beijing, China;Department of Industrial Engineering, Tsinghua University, Beijing, China;Department of Industrial Engineering, Tsinghua University, Beijing, China;Production System, Caterpillar Incorporation, Peoria, IL
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

Citing 8
Cited 0

An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Pattern Recognition
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image Segmentation Based on Adaptive Cluster Prototype Estimation

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

As a critical unit of computer vision (CV) based applications, image segmentation is quite worth studying. Hybrid method of spatial credibilistic clustering and particle swarm optimization (SCCPSO) [1] is a novel effective segmentation method. It's proved to produce better results than other common methods. In this paper, SCCPSO is further investigated by discussing several key points such as membership function, initialization, pre-selection, and boundary conditions. Then the modified SCCPSO is put forth and applied in a CV-based inspection system to show its effectivity and better performance. The proposed method can be also used in other CV-based applications.