Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

Determinationofoptimumthresholds is theprimeconcernofanymultilevel imagethresholding technique.Thetraditionalmethodsformultilevelthresholdingarecomputationallyexpensive,timeconsuming,andalsosufferfromlackofaccuracyandstability.Toaddressthisissue,theauthors proposeanewmethodologyformultilevelimagethresholdingbasedonarecentlydevelopedmetaheuristicalgorithm,SymbioticOrganismsSearch(SOS).TheSOSalgorithmhasbeeninspiredby thesymbioticrelationshipamongtheorganisminnature.Thisarticlehasutilizedtheconceptofthe symbioticrelationshipamongtheorganismstooptimizethreeobjectivefunctions:Otsu’sbetween classvarianceandKapur’sandTsallis entropy for image segmentation.Theperformanceof the SOSbasedimagesegmentationalgorithmhasbeenevaluatedusingasetofbenchmarkimagesand has been comparedwith four recentmeta-heuristic algorithms.The algorithms are compared in termsofeffectivenessandconsistency.Thequalityofthealgorithmshasbeenestimatedbysome well-definedqualitymetricssuchaspeaksignal-to-noiseratio(PSNR),structuresimilarityindex (SSIM),and,featuresimilarityindex(FSIM).Theexperimentalresultsofthealgorithmsrevealthat thebalanceofintensificationanddiversificationoftheSOSalgorithmtoachievetheglobaloptima isbetterthanothers. KeywORdS Entropy, Image Segmentation, Multi-Level Thresholding, Nature Inspired Optimization, Symbiotic Organisms Search

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