A Hybrid Clustering Based Color Image Segmentation using Ant Colony and Particle Swarm Optimization Methods
V.Sheshathri1, S.Sukumaran2
1V.Sheshathri, Ph.D Research Scholar, Department of Computer Science, Erode Arts and Science College, (Tamilnadu), India.
2Dr.S.Sukumaran, Associate Professor, Department of Computer Science, Erode Arts and Science College, (Tamilnadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 352-358 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5256058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image segmentation is one of the most significant ways to simplify complex images into human or machine readable form. The main purpose of image segmentation ways is to extract or segment out particular area or region of image. It can also be used to separate foreground image from the background image. Image segmentation methods for depicting images have gained a great achievements but the color image segmentation method based on statistical theory have exposed some limitation. The color image segmentation main purpose is to reduce the undesired limitations of the conventional segmentation method. Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are the two main methods for swarm intelligence have great potential in color image segmentation method. This paper introduces color image segmentation using the Hybrid Clustering based Ant Colony Optimization and Particle Swarm Optimization methods. The experimental result of segmentation method has been evaluated by determining the PSNR and accuracy values of the input images. The proposed HCACOPSO method is compared with the existing methods of Otsu and CPSO-FCM methods which gives better result.
Keyword: Background Image, Particle Swarm Optimization, Ant Colony Optimization, Region, Segmentation
Scope of the Article: Clustering.