@Article{cmc.2019.04069,
AUTHOR = {Zhe  Liu, Bao  Xiang, Yuqing  Song, Hu  Lu, Qingfeng  Liu},
TITLE = {An Improved Unsupervised Image Segmentation Method Based on Multi-Objective Particle Swarm Optimization Clustering Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {58},
YEAR = {2019},
NUMBER = {2},
PAGES = {451--461},
URL = {http://www.techscience.com/cmc/v58n2/23028},
ISSN = {1546-2226},
ABSTRACT = {Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm.},
DOI = {10.32604/cmc.2019.04069}
}