{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:22:06Z","timestamp":1781108526025,"version":"3.54.1"},"reference-count":40,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,10,1]]},"abstract":"<p>Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding (AMT) problem. The goal is to determine the correct number of thresholds as well as their optimal values. For this purpose, the article adopts\u2014for each metaheuristic\u2014a new hybrid coding scheme such that each individual solution is represented by two parts: a real part which represents the thresholds values and a binary part which indicates if a given threshold will be used or not during the thresholding process. Experiments have been conducted on six real test images and the results have been compared with two automatic multilevel thresholding based PSO methods and the exhaustive search method for fair comparison. Empirical results reveal that AMT-HABC and AMT-HCS algorithms performed equally to the solution provided by the exhaustive search and are better than the other comparison algorithms. In addition, the results indicate that the ATM-HABC algorithm has a higher success rate and a speed convergence than the other metaheuristics.<\/p>","DOI":"10.4018\/ijamc.2014100103","type":"journal-article","created":{"date-parts":[[2014,11,25]],"date-time":"2014-11-25T07:48:23Z","timestamp":1416901703000},"page":"47-69","source":"Crossref","is-referenced-by-count":4,"title":["Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding"],"prefix":"10.4018","volume":"5","author":[{"given":"Salima","family":"Ouadfel","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Constantine 2, Nouvelle ville Ali Mendjeli, Constantine, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Souham","family":"Meshoul","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Constantine 2, Nouvelle ville Ali Mendjeli, Constantine, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijamc.2014100103-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.07.015"},{"key":"ijamc.2014100103-1","unstructured":"Alihodzic A and Tuba M, (2013) \u201cBat Algorithm (BA) for Image Thresholding\u201d, Recent Researches in Telecommunications, Informatics, Electronics and Signal Processing"},{"key":"ijamc.2014100103-2","unstructured":"Brajevic Ivona, Tuba Milan, Bacanin Nebojsa Multilevel Image Thresholding Selection Based on the Cuckoo Search Algorithm. In Advances in Sensors, Signals, Visualization, Imaging and Simulation"},{"key":"ijamc.2014100103-3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13312-1_12"},{"key":"ijamc.2014100103-4","doi-asserted-by":"publisher","DOI":"10.1109\/SoCPaR.2009.25"},{"key":"ijamc.2014100103-5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2006.11.005"},{"key":"ijamc.2014100103-6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2009.2030931"},{"key":"ijamc.2014100103-7","author":"D. E.Goldberg","year":"1989","journal-title":"\u2018Genetic Algorithms in Search\u2019, Optimization, and Machine Learning"},{"key":"ijamc.2014100103-8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2007.09.001"},{"key":"ijamc.2014100103-9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.12.050"},{"key":"ijamc.2014100103-10","first-page":"13785","article-title":"\u2018Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation\u2019.","volume":"38","author":"M.-H.Horng","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"ijamc.2014100103-11","doi-asserted-by":"publisher","DOI":"10.1016\/0734-189X(85)90125-2"},{"key":"ijamc.2014100103-12","unstructured":"Karaboga, D. (2005) \u2018An idea based on honey bee swarm for numerical optimization\u2019, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department."},{"key":"ijamc.2014100103-13","first-page":"1948","article-title":"Particle swarm optimization","volume":"vol. 4","author":"J.Kennedy","year":"1995","journal-title":"Proc.IEEE Int. Conf. Neural Netw"},{"key":"ijamc.2014100103-14","unstructured":"Kennedy, J., & Eberhart, R. C. (1997) A discrete binary version of the particle swarm algorithm, in Procs. of the Conf. on SMC97, Piscataway, NJ, USA, (1997) 4104-4109."},{"key":"ijamc.2014100103-15","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(86)90030-0"},{"key":"ijamc.2014100103-16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.01.002"},{"key":"ijamc.2014100103-17","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"\u2018A threshold selection method from gray level histograms\u2019.","volume":"SMC-9","author":"N.Otsu","year":"1979","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"ijamc.2014100103-18","doi-asserted-by":"publisher","DOI":"10.5815\/ijigsp.2013.01.07"},{"key":"ijamc.2014100103-19","doi-asserted-by":"publisher","DOI":"10.1504\/IJCAT.2014.062358"},{"key":"ijamc.2014100103-20","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00111-5"},{"key":"ijamc.2014100103-21","doi-asserted-by":"publisher","DOI":"10.1109\/MCS.2002.1004010"},{"key":"ijamc.2014100103-22","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-011-9276-0"},{"key":"ijamc.2014100103-23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/685175","article-title":"Analysis of the publications on the applications of particle swarm optimization","volume":"\u25aa\u25aa\u25aa","author":"R.Poli","year":"2008","journal-title":"Journal of Artificial Evolution and Applications"},{"key":"ijamc.2014100103-24","doi-asserted-by":"crossref","unstructured":"S-Hosseini, H. (2012), Intelligent water drops algorithm for automatic multilevel thresholding of grey-level images using a modified Otsu\u2019s criterion. Int. J. Modelling, Identification and Control, 15, 4, 241","DOI":"10.1504\/IJMIC.2012.046402"},{"key":"ijamc.2014100103-25","doi-asserted-by":"crossref","unstructured":"Sanjay A, Rutuparna P, Sudipta B, B.K. Panigrahib, (2013) \u201cTsallis entropy based optimal multilevel thresholding using cuckoo search algorithm\u201d, Swarm and Evolutionary Computation Volume 11, August, Pages 16\u201330","DOI":"10.1016\/j.swevo.2013.02.001"},{"key":"ijamc.2014100103-26","doi-asserted-by":"crossref","unstructured":"Sarkar, S. Gyana RanjanPatra, and Das, S. A (2011) \u2018Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding\u2019. SEMCCO 1. pp. 51-58.","DOI":"10.1007\/978-3-642-27172-4_7"},{"key":"ijamc.2014100103-27","first-page":"65","author":"P. D.Sathya","year":"2011","journal-title":"\u2018Modified bacterial foraging algorithm based multilevel thresholding for image segmentation\u2019. Engineering Applications of Artificial Intelligence"},{"issue":"January","key":"ijamc.2014100103-28","first-page":"146","article-title":"\u2018Survey over image thresholding techniques and quantitative performance\u2019.","volume":"13","author":"M.Sezgin","year":"2004","journal-title":"Journal of Electronic Imaging"},{"key":"ijamc.2014100103-29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2008.09.001"},{"key":"ijamc.2014100103-30","unstructured":"Storn, R., & Price, K. (1995) \u2018Differential Evolution \u2014 a simple and efficient heuristic for global optimization over continuous spaces\u2019. Technical Report TR-95-012.,ICSI."},{"key":"ijamc.2014100103-31","first-page":"111","article-title":"A global search strategy of quantum-behaved particle swarm optimization","volume":"vol. 1","author":"J.Sun","year":"2004","journal-title":"Proc. IEEE Int. Conf. Cybern. Intell. Syst."},{"key":"ijamc.2014100103-32","author":"X. S.Yang","year":"2008","journal-title":"Nature-Inspired Metaheuristic Algorithms"},{"key":"ijamc.2014100103-33","doi-asserted-by":"crossref","unstructured":"Yang, X. S. (2010) A new metaheuristic BA-inspired algorithm. Nature Inspired Cooperative Strategies for Optimisation (NICO 2010), pages 65-74, 2011.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ijamc.2014100103-34","doi-asserted-by":"crossref","unstructured":"Yang;X.-S. Deb, S (2009). \u201cCuckoo search via L\u00e9vy flights\u201d. World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE Publications. pp. 210\u2013214.","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"ijamc.2014100103-35","doi-asserted-by":"publisher","DOI":"10.1109\/ICICTA.2008.51"},{"key":"ijamc.2014100103-36","unstructured":"Yen, J.C., Chang, F.J. & Chang, S.(1995) A new criterion for automatic multilevel thresholding, IEEE Trans. Image 78."},{"key":"ijamc.2014100103-37","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.06.057"},{"key":"ijamc.2014100103-38","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-1684(98)00167-4"},{"key":"ijamc.2014100103-39","doi-asserted-by":"publisher","DOI":"10.1109\/IMSCCS.2006.280"}],"container-title":["International Journal of Applied Metaheuristic Computing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=119648","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T19:57:28Z","timestamp":1654113448000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijamc.2014100103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2014,10,1]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijamc.2014100103","relation":{},"ISSN":["1947-8283","1947-8291"],"issn-type":[{"value":"1947-8283","type":"print"},{"value":"1947-8291","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,10,1]]}}}