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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1007/bf00866763

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1573-7721

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A hybrid bioinspired learning algorithm for image

Authors: Mohammad Mahdi Dehshibi Mohamad Sourizaei Mahmood Fazlali Omid Talaee Hossein Samadyar Jamshid Shanbehzadeh
Publish Date: 2016/09/21
Volume: 76, Issue: 14, Pages: 15951-15986
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Abstract

In the field of image analysis segmentation is one of the most important preprocessing steps One way to achieve segmentation is the use of threshold selection where each pixel that belongs to a determined class based on the mutual visual characteristics is labeled according to the selected threshold In this work a combination of two pioneer methods namely Otsu and Kapur are investigated to solve the threshold selection problem Optimum parameters of these objective functions are calculated using Bacterial Foraging BF optimization algorithm for its accuracy and Harmony Search HS for its speed However the biggest problem of soft computing family algorithms is catching into a local optimum To resolve this critical issue we investigate the power of Learning Automata LA which works as a controller to make switching between these two optimization methods LA is a heuristic method which can solve complex optimization problems with interesting results in parameter estimation Despite other techniques commonly seek through the parameter map LA explores in the probability space providing appropriate convergence properties and robustness The proposed method is tested on benchmark images and shows fast convergence avoiding the typical sensitivity to initial conditions such as the ExpectationMaximization EM algorithm or the complex and timeconsuming computations which are commonly found in gradient methods Experimental results demonstrate the algorithm’s ability to perform automatic multithreshold selection and show interesting advantages as it is compared to other algorithms solving the same task

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