@article{Zhao2016151,
title = "A multilevel image thresholding segmentation algorithm based on two-dimensional K–L divergence and modified particle swarm optimization ",
journal = "Applied Soft Computing ",
volume = "48",
number = "",
pages = "151 - 159",
year = "2016",
note = "",
issn = "1568-4946",
doi = "http://dx.doi.org/10.1016/j.asoc.2016.07.016",
url = "http://www.sciencedirect.com/science/article/pii/S1568494616303428",
author = "Xiaoli Zhao and Matthew Turk and Wei Li and Kuo-chin Lien and Guozhong Wang",
keywords = "2D K–L divergence",
keywords = "Modified PSO",
keywords = "Multilevel image thresholding segmentation ",
abstract = "Abstract Multilevel image segmentation is a technique that divides images into multiple homogeneous regions. In order to improve the effectiveness and efficiency of multilevel image thresholding segmentation, we propose a segmentation algorithm based on two-dimensional (2D) Kullback–Leibler(K–L) divergence and modified Particle Swarm Optimization (MPSO). This approach calculates the 2D K–L divergence between an image and its segmented result by adopting 2D histogram as the distribution function, then employs the sum of divergences of different regions as the fitness function of \{MPSO\} to seek the optimal thresholds. The proposed 2D K–L divergence improves the accuracy of image segmentation; the \{MPSO\} overcomes the drawback of premature convergence of \{PSO\} by improving the location update formulation and the global best position of particles, and reduces drastically the time complexity of multilevel thresholding segmentation. Experiments were conducted extensively on the Berkeley Segmentation Dataset and Benchmark (BSDS300), and four performance indices of image segmentation – BDE, PRI, \{GCE\} and \{VOI\} – were tested. The results show the robustness and effectiveness of the proposed algorithm. "
}