Weitere Kapitel dieses Buchs durch Wischen aufrufen
Segmentation is used to divide an image into separate regions, which in fact correspond to different real-world objects. One interesting functional criterion for segmentation is the Tsallis entropy (TE), which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding (MT), its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. In this chapter, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm (EMO) is presented. In the approach, the EMO algorithm is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Cuevas, E., Zaldivar, D., Pérez-Cisneros, M., Seeking multi-thresholds for image segmentation with Learning Automata, Machine Vision and Applications, 22 (5), (2011), 805–818.
Y. Kong, Y. Deng, Q. Dai, and S. Member, “Discriminative Clustering and Feature Selection for Brain MRI Segmentation,” IEEE Signal Process. Lett., vol. 22, no. 5, pp. 573–577, 2015.
X. Cao, Q. Li, X. Du, M. Zhang, and X. Zheng, “Exploring effect of segmentation scale on orient-based crop identification using HJ CCD data in Northeast China,” IOP Conf. Ser. Earth Environ. Sci., vol. 17, p. 012047, 2014.
A. K. Bhandari, V. K. Singh, A. Kumar, and G. K. Singh, “Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy,” Expert Syst. Appl., vol. 41, no. 7, pp. 3538–3560, 2014.
S. Sarkar and S. Das, “Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy—A Differential Evolution Approach,” Lect. Notes Comput. Sci., vol. 22, no. 12, pp. 4788–4797, 2013.
B. Akay, “A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding,” Appl. Soft Comput., vol. 13, no. 6, pp. 3066–3091, 2012.
H. Xia, S. Song, and L. He, “A modified Gaussian mixture background model via spatiotemporal distribution with shadow detection,” Signal, Image Video Process., 2015.
G. Moser, S. B. Serpico, and S. Member, “Generalized Minimum-Error Thresholding for Unsupervised Change Detection From SAR Amplitude Imagery.pdf,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2972–2982, 2006.
Sezgin M, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging, vol. 13, no. January, pp. 146–168, 2004.
N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62–66, 1979.
A. K. C. J. N. Kapur, P. K. Sahoo, A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram.” Computer Vision Graphics Image Processing, pp. 273–285, 1985.
P. D. Sathya and R. Kayalvizhi, “Optimal multilevel thresholding using bacterial foraging algorithm,” Expert Syst. Appl., vol. 38, no. 12, pp. 15549–15564, 2011.
S. Agrawal, R. Panda, S. Bhuyan, and B. K. Panigrahi, “Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm,” Swarm Evol. Comput., vol. 11, pp. 16–30, 2013.
C. Tsallis, “Possible generalization of Boltzmann-Gibbs statistics,” J. Stat. Phys., vol. 52, pp. 479–487, 1988.
E. K. Tang, P. N. Suganthan, and X. Yao, “An analysis of diversity measures,” Mach. Learn., vol. 65, no. April, pp. 247–271, 2006.
Y. Zhang and L. Wu, “Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach,” Entropy, vol. 13, pp. 841–859, 2011.
C. Tsallis, “Computational applications of nonextensive statistical mechanics,” J. Comput. Appl. Math., vol. 227, no. 1, pp. 51–58, 2009.
Cuevas, E., Ortega-Sánchez, N., Zaldivar, D., Pérez-Cisneros, M., Circle detection by Harmony Search Optimization, Journal of Intelligent and Robotic Systems: Theory and Applications, 66(3), (2012), 359–376.
N. Sri, M. Raja, G. Kavitha, and S. Ramakrishnan, “Analysis of Vasculature in Human Retinal Images Using Particle Swarm Optimization Based Tsallis Multi-level Thresholding and Similarity Measures,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7677, no. 1, pp. 380–387, 2012.
Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Perez-Cisneros, M., Multilevel thresholding segmentation based on harmony search optimization, Journal of Applied Mathematics, 2013, 575414.
Ş. I. Birbil and S. C. Fang, “An electromagnetism-like mechanism for global optimization,” J. Glob. Optim., vol. 25, no. 1, pp. 263–282, 2003.
A. M. A. C. Rocha and E. M. G. P. Fernandes, “Modified movement force vector in an electromagnetism-like mechanism for global optimization,” Optim. Methods Softw., vol. 24, no. 2, pp. 253–270, 2009.
H. L. Hung and Y. F. Huang, “Peak to average power ratio reduction of multicarrier transmission systems using electromagnetism-like method,” Int. J. Innov. Comput. Inf. Control, vol. 7, no. 5, pp. 2037–2050, 2011.
A. Yurtkuran and E. Emel, “A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems,” Expert Syst. Appl., vol. 37, no. 4, pp. 3427–3433, 2010.
J. Y. Jhang and K. C. Lee, “Array pattern optimization using electromagnetism-like algorithm,” AEU - Int. J. Electron. Commun., vol. 63, pp. 491–496, 2009.
C. H. Lee and F. K. Chang, “Fractional-order PID controller optimization via improved electromagnetism-like algorithm,” Expert Syst. Appl., vol. 37, no. 12, pp. 8871–8878, 2010.
L. N. De Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE Trans. Evol. Comput., vol. 6, no. 3, pp. 239–251, 2002.
A. M. A. C. Rocha and E. M. G. P. Fernandes, “Hybridizing the Electromagnetism-like algorithm with Descent Search for Solving Engineering Design Problems,” Int. J. Comput. Math., vol. 86, no. 10–11, pp. 1932–1946, 2009.
P. Ghamisi, M. S. Couceiro, J. A. Benediktsson, and N. M. F. Ferreira, “An efficient method for segmentation of images based on fractional calculus and natural selection,” Expert Syst. Appl., vol. 39, no. 16, pp. 12407–12417, 2012.
P. Wu, W.-H. Yang, and N.-C. Wei, “An Electromagnetism Algorithm of Neural Network Analysis—an Application To Textile Retail Operation,” J. Chinese Inst. Ind. Eng., vol. 21, no. 1, pp. 59–67, 2004.
K. De Jong, “Learning with genetic algorithms: An overview,” Mach. Learn., vol. 3, pp. 121–138, 1988.
B. Naderi, R. Tavakkoli-Moghaddam, and M. Khalili, “Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan,” Knowledge-Based Syst., vol. 23, no. 2, pp. 77–85, 2010.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.
D. Z. Lin Zhang, Lei Zhang, XuanqinMou, “FSIM : A Feature Similarity Index for Image,” IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378–2386, 2011.
C. Tsallis, “Entropic nonextensivity: A possible measure of complexity,” Chaos, Solitons and Fractals, vol. 13, pp. 371–391, 2002.
S. García, D. Molina, M. Lozano, and F. Herrera, “A Study on the Use of Non-Parametric Tests for Analyzing the Evolutionary Algorithms’ Behaviour: A Case Study on the CEC 2005 Special Session on Real Parameter Optimization,” J. Heuristics, vol. 15, pp. 617–644, 2009.
- Multilevel Segmentation in Digital Images
- Chapter 2
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia