A multilevel image thresholding using the animal migration optimization algorithm
Tóm tắt
Thresholding is an important and well-known technique that plays a major role in distinguishing the image objects from its background. In the other hand, separating the images into several different regions by determining multiple threshold values is called multilevel image thresholding. The Kapur entropy thresholding and maximum between-class variance (Otsu) have been widely used in image thresholding. However, these methods are computationally expensive and with increase in level numbers computational complexity increase exponentially. To overcome this problem, this paper presents animal migration optimization algorithm for multilevel thresholding. For evaluating the efficiency of proposed method, various benchmark images are used for carrying out the experiments, and obtained results via animal migration optimization algorithm compared with most popular optimization technique such as Particle Swarm Optimization, Genetic and bacterial foraging algorithm. Experimental results figure out that the proposed method provides better result than the other tested algorithms.
Tài liệu tham khảo
Sarkar, S., Das, S., Chaudhuri, S.S.: A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn. Lett. 54, 27–35 (2015)
Horng, M.-H., Liou, R.-J.: Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst. Appl. 38(12), 14805–14811 (2011)
Chang, C.-C., Wang, L.-L.: A fast multilevel thresholding method based on lowpass and highpass filtering. Pattern Recogn. Lett. 18(14), 1469–1478 (1997)
Kurban, T., Civicioglu, P., Kurban, R., Besdok, E.: Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl. Soft Comput. 23, 128–143 (2014)
Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Perez-Cisneros, M.: Multilevel thresholding segmentation based on harmony search optimization. J. Appl. Math. (2013)
Arora, S., Acharya, J., Verma, A., Panigrahi, P.K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn. Lett. 29(2), 119–125 (2008)
Maitra, M., Chatterjee, A.: A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst. Appl. 34(2), 1341–1350 (2008)
Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)
Pun, T.: Entropic thresholding, a new approach. Comput. Graph. Image Process. 16(3), 210–239 (1981)
Brink, A.: Minimum Spatial Entropy Threshold Selection. In: Vision, image and signal processing, IEE proceedings, vol. 3. IET, pp 128–132 (1995)
Cheng, H., Chen, J.-R., Li, J.: Threshold selection based on fuzzy c-partition entropy approach. Pattern Recogn. 31(7), 857–870 (1998)
Li, X., Zhao, Z., Cheng, H.: Fuzzy entropy threshold approach to breast cancer detection. Inf. Sci. Appl. 4(1), 49–56 (1995)
Tobias, O.J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans. Image Process. 11(12), 1457–1465 (2002)
Huang, L.-K., Wang, M.-J.J.: Image thresholding by minimizing the measures of fuzziness. Pattern Recogn. 28(1), 41–51 (1995)
de Albuquerque, M.P., Esquef, I.A., Mello, A.G.: Image thresholding using Tsallis entropy. Pattern Recogn. Lett. 25(9), 1059–1065 (2004)
Liao, P.-S., Chen, T.-S., Chung, P.-C.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713–727 (2001)
Yin, P.-Y., Chen, L.-H.: A fast iterative scheme for multilevel thresholding methods. Signal Process. 60(3), 305–313 (1997)
Cao, L., Shi, Z., Cheng, E.: Fast automatic multilevel thresholding method. Electron. Lett. 38(16), 868–870 (2002)
Pikaz, A., Averbuch, A.: Digital image thresholding, based on topological stable-state. Pattern Recogn. 29(5), 829–843 (1996)
Hertz, L., Schafer, R.W.: Multilevel thresholding using edge matching. Comput Vis Graph Image Process 44(3), 279–295 (1988)
Sathya, P., Kayalvizhi, R.: Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng. Appl. Artif. Intell. 24(4), 595–615 (2011)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–166 (2004)
Sathya, P., Kayalvizhi, R.: Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement 44(10), 1828–1848 (2011)
Sathya, P., Kayalvizhi, R.: Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14), 2299–2313 (2011)
Yin, P.-Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)
Pal, S.S., Kumar, S., Kashyap, M., Choudhary, Y., Bhattacharya, M.: Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm. In: Proceedings of the second international conference on computer and communication technologies. Springer, Berlin, pp 273–287 (2016)
Horng, M.-H.: Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl. 38(11), 13785–13791 (2011)
Li, X., Zhang, J., Yin, M.: Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput. Appl. 24(7–8), 1867–1877 (2014)