Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function
R. Pugalenthi1, A. Sheryl Oliver2
1BR.Pugalenthi, Associate Professor, Department of CSE, St. Joseph’s College of Engineering, Chennai, India..
2A.Sheryl Oliver, Associate Professor, Department of CSE, St. Joseph’s College of Engineering, Chennai, India.
Manuscript received on 27 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 1719-1734 | Volume-8 Issue-11, September 2019. | Retrieval Number: K15260981119/2019©BEIESP | DOI: 10.35940/ijitee.K1526.0981119
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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: A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.
Keywords: Image thresholding, Tsallis function, Fractional-order Darwinian PSO, Robustness analysis, Image quality measures, Wilcoxon’s test.
Scope of the Article: Image analysis and Processing