Optimal Multilevel Image Thresholding: An Analysis with PSO and BFO Algorithms

Rajinikanth, V.; Raja, N. Sri Madhava; Latha, K.
June 2014
Australian Journal of Basic & Applied Sciences;Jun2014, Vol. 8 Issue 9, p443
Academic Journal
Multilevel thresholding is widely adopted in image processing and pattern recognition fields. In this paper, Otsu based bi-level and multi-level image segmentation problem is addressed using Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) algorithms. Optimal thresholds are attained by analyzing histogram of the test image. Maximization of Otsu's between class variance function is adopted to guide the heuristic algorithm based exploration. Performance of the proposed method is tested on eight benchmark test images using various numbers of thresholds. An assessment between PSO (constant weight), PSO (varying weight), Adaptive BFO, and Enhanced BFO are performed and the experimental results are validated using well known statistical parameters. For a bi-level optimization problem, considered heuristic algorithms show equal performance. For increase in threshold levels, PSO (constant weight) offers faster convergence and Enhanced BFO provides better structural similarity (SSIM) index.


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