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Publication Type : Conference Paper
Publisher : Computational Vision and Bio-Inspired Computing, Springer International Publishing
Source : Computational Vision and Bio-Inspired Computing, Springer International Publishing, Cham (2020)
Url : https://link.springer.com/chapter/10.1007%2F978-3-030-37218-7_38
ISBN : 9783030372187
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : Image segmentation is an activity of dividing an image into multiple segments. Thresholding is a typical step for analyzing image, recognizing the pattern, and computer vision. Threshold value can be calculated using histogram as well as using Gaussian mixture model. but those threshold values are not the exact solution to do the image segmentation. To overcome this problem and to find the exact threshold value, differential evolution algorithm is applied. Differential evolution is considered to be meta-heuristic search and useful in solving optimization problems. DE algorithms can be applied to process Image Segmentation by viewing it as an optimization problem. In this paper, Different Differential evolution (DE) algorithms are used to perform the image segmentation and their performance is compared in solving image segmentation. Both 2 class and 3-class segmentation is applied and the algorithm performance is analyzed. Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms
Cite this Research Publication : V. SandhyaSree and Dr. Thangavelu S., “Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation”, in Computational Vision and Bio-Inspired Computing, Cham, 2020.