Research Article
Image Clustering Using Improved Particle Swarm Optimization
@INPROCEEDINGS{10.1007/978-3-319-74176-5_31, author={Thuy Pham and Patrick Siarry and Hamouche Oulhadj}, title={Image Clustering Using Improved Particle Swarm Optimization}, proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings}, proceedings_a={INISCOM}, year={2018}, month={1}, keywords={Image segmentation Particle swarm optimization Entropy based fuzzy clustering Fitness function}, doi={10.1007/978-3-319-74176-5_31} }- Thuy Pham
Patrick Siarry
Hamouche Oulhadj
Year: 2018
Image Clustering Using Improved Particle Swarm Optimization
INISCOM
Springer
DOI: 10.1007/978-3-319-74176-5_31
Abstract
In this paper, we propose an improvement method for image segmentation problem using particle swarm optimization (PSO) with a new objective function based on kernelization of improved fuzzy entropy clustering algorithm with spatial local information, called PSO-KFECS. The main objective of our proposed algorithm is to segment accurately images by utilizing the state-of-the-art development of PSO in optimization with a novel fitness function. The proposed PSO-KFECS was evaluated on several benchmark test images including synthetic images (), and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb ()). Experimental results show that our proposed PSO-KFECS algorithm can perform better than the competing algorithms.
