Multilevel minimum cross entropy threshold selection based on the firefly algorithm |
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| Authors: | Ming-Huwi Horng Ren-Jean Liou |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Pingtung Institute of Commerce, Pingtung, Taiwan;2. Department of Computer and Communication, National Pingtung Institute of Commerce, Pingtung, Taiwan;1. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución, 1500, Guadalajara, Jal, Mexico;2. Department of Electrical Engineering, UG-IV Jadavpur University, Kolkata, India;3. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt;4. Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, 28040, Madrid, Spain;5. Department of Medical Biophysics, University of Toronto, Toronto, Canada;1. PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, India;2. Department of Electrical Engineering, Indian Institute Technology Roorkee, Uttarakhand 247667, India;1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an 710071, China;2. School of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK;1. Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain;2. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico;3. Departamento de Ingenierías, Universidad de Guadalajara, CUTONALA, Sede Provisional Casa de la Cultura – Administración: Calle Morelos 180, Tonalá, Jalisco, Mexico |
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| Abstract: | The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm. |
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