Segmention of images using a modified Cuckoo Search (CSMcCulloch) algorithm

Implementation of modified CS algorithm using McCulloch's method for Levy flight generation

A computationally efficient image segmentation algorithm, called CSMcCulloch , incorporating McCulloch’s method for levy flight generation in Cuckoo Search (CS) algorithm) is introduced. CS is a meta-heuristic optimization algorithm evolved mimicking the captivating reproduction policy of certain Cuckoo species developed by Yang and Deb (2009). CSMcCulloch algorithm is evaluated for segmenting gray/color images using Otsu’s method, Kapur entropy and Tsallis entropy as objective functions. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images (particularly satellite images).
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This is a code for Segmenting Gray/RGB image using a modified Cuckoo Search algorithm (CSMcCulloch) tested with different objective functions.
CSMC_otsu.m : The function which can be run to view a sample result of segmenting a gray or RGB image using CS MCulloch algorithm with Otsu's between class variance as objective function
CSMC_kapur.m : The function which can be run to view a sample result of segmenting a gray or RGB image using CS MCulloch algorithm with Kapur's entropy as objective function
CSMC_tsallis.m : The function which can be run to view a sample result of segmenting a gray or RGB image using CS MCulloch algorithm with Tsallis entropy as objective function
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Please refer to this paper for further information:Shilpa Sureshand Shyam Lal, "An Efficient Cuckoo Search Algorithm based Multilevel Thresholding for Segmentation of Satellite Images Using Different Objective Functions, Expert Systems with Applications, vol. 58, pp. 184-209, October 2016, Elsevier Publisher, Article DOI: 10.1016/j.eswa.2016.03.032.
Available online: http://www.sciencedirect.com/science/article/pii/S0957417416301348