Applied Mathematics & Information Sciences
Abstract
Abstract: Image segmentation is applied widely to image processing and object recognition. Threshold segmentation is a simple and important method in grayscale image segmentation. Information entropy can characterize the grayscale in formation of image and distinguish between the objectives and background. In this paper, we use exponential entropy instead of logarithmic entropy and propose a new multilevel thresholds image segmentation method based on maximum entropy and adaptive Particle Swarm Optimization (APSO). This proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. The APSO takes advantage of the characteristics of particle swarm optimization, through adaptively adjust particles flying speed to improve evolutional process of basic PSO. Standard test images and remote sensing image are segmented in experiment and compared with other related segmentation methods. Experimental results show that the APSO method can quickly converge with high computational efficiency.
Suggested Reviewers
N/A
Digital Object Identifier (DOI)
http://dx.doi.org/10.12785/amis/080654
Recommended Citation
Qi, Chengming
(2014)
"Maximum Entropy for Image Segmentation based on an Adaptive Particle Swarm Optimization,"
Applied Mathematics & Information Sciences: Vol. 08:
Iss.
6, Article 54.
DOI: http://dx.doi.org/10.12785/amis/080654
Available at:
https://digitalcommons.aaru.edu.jo/amis/vol08/iss6/54