A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

Shreya Pare; Anil Kumar; Varun Bajaj; Girish Kumar Singh

doi:10.1109/JAS.2017.7510697

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

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Shreya Pare, Anil Kumar, Varun Bajaj and Girish Kumar Singh, "A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1471-1486, Nov. 2019. doi: 10.1109/JAS.2017.7510697

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Shreya Pare, Anil Kumar, Varun Bajaj and Girish Kumar Singh, "A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1471-1486, Nov. 2019. doi: 10.1109/JAS.2017.7510697

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A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

doi: 10.1109/JAS.2017.7510697

1.
PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, MP, India
2.
Indian Institute of Technology Roorkee, Uttarakhand 247667, India

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Author Bio:

Shreya Pare received the B. E. (Hons.) degree in electronics and communication engineering from Rajiv Gandhi Technological University, Bhopal, Indiain 2011 and M.E (Hons.) degree from Shri Govindram Seksaria Institute of Technology & Science, Indore, India in 2014 and currently pursuing Ph.D. in electronics and communication engineering, PDPM-Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India. Her research interests include satellite image segmentation, image enhancement, image denoising and optimization techniques. (e-mail: shreya.pare9@gmail.com)

Anil Kumar received the B. E. degree from the Army Institute of Technology (AIT), Pune University, Maharashtra, India, and the M. Tech and Ph.D. degrees from IIT Roorkee, India, in 2002, 2006, and 2010, respectively, all in electronic and telecommunication engineering. He is an Assistant Professor in the Electronic and Communication Engineering Department, PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India. Currently, he is a Visiting Researcher at the School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Korea. His research interests include design of digital filters and multirate filter bank, multirate signal processing, biomedical signal processing, image processing, and speech processing. (e-mail: anilkdee@gmail.com)

Varun Bajaj received the B.E. degree in electronics and communication engineering from Rajiv Gandhi Technological University, Bhopal, India, in 2006. He received the M.Tech. (Hons.) degree in microelectronics and VLSI design from Shri Govindram Seksaria Institute of Technology & Science, Indore, India, in 2009. He received the Ph.D. degree in the discipline of electrical engineering, at Indian Institute of Technology Indore, Indore, India. Presently, he is working as Assistant Professor with the discipline of electronics and communication engineering, at PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India. His research interests include biomedical signal processing, image processing and time-frequency analysis, speech processing. (e-mail: bajajvarun056@yahoo.co.in)

Girish Kumar Singh received the B. Tech degree from G. B. Pant University of Agriculture and Technology, Pantnagar, India, in 1981, and Ph.D. degree from Banaras Hindu University, Varanasi, India, in 1991, both in electrical engineering. He worked in the industry for nearly five and a half years. Currently, he is a Professor in the Department of Electrical Engineering, IIT Roorkee, India. His academic and research interests include design and analysis of electrical machines and biomedical signal processing. He has coordinated a number of research projects sponsored by the CSIR and UGC, Government of India. (e-mail: gksngfee@gmail.com)
Corresponding author:
Shreya Pare, e-mail: shreya.pare9@gmail.com
Received Date: 2016-04-08
Revised Date: 2016-04-12
Accepted Date: 2017-10-31

Available Online: 2019-10-22

Publish Date: 2019-10-22

Abstract

Abstract

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.
- Color image segmentation,
- Kapur's entropy,
- multilevel thresholding,
- Otsu method,
- swarm based optimization algorithms,
- Tsalli's entropy

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References(46)

References

[1]	P. K. Saha and J. K. Udupa, "Optimum image thresholding via class uncertainty and region homogeneity, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 689-706, 2001. doi: 10.1109/34.935844
[2]	J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, "A new method for graylevel picture thresholding using the entropy of the histogram, " Computer Vision Graphics Image Processing, vol. 29, no. 3, pp. 273-285, 1985. doi: 10.1016/0734-189X(85)90125-2
[3]	W. H. Tsai, "Moment-preserving thresholding:a new approach. Computer Vision, " Graphics, and Image Processing, vol. 29, no. 3, pp. 377-393, 1985. doi: 10.1016-0734-189X(85)90133-1/
[4]	J. C. Park and S. T. Abusalah, "Maximum entropy:a special case of minimum cross-entropy applied to nonlinear estimation by an artificial neural network, " Complex Systems, vol. 11, no. 4, pp. 289-308, 1997.
[5]	M. H. Merzban and M. Elbayoumi, "Efficient solution of Otsu multilevel image thresholding:a comparative study, "Expert Systems with Applications, vol. 116, pp. 299-309, 2019. doi: 10.1016/j.eswa.2018.09.008
[6]	S. Pare, A. Kumar, G. K. Singh, and V. Bajaj, " Image segmentation using multilevel thresholding: a research review, "Iranian J. Science and Technology, Trans. Electrical Engineering, 2019, DOI: org/10.1007/s40998-019-00251-1.
[7]	S. Suresh and S. Lal, " Multilevel thresholding based on chaotic darwinian particle swarm optimization for segmentation of satellite images, Applied Soft Computing, vol. 55, pp. 503-522, 2017.
[8]	S. Borjigin and P. K. Sahoo, "Color image segmentation based on multi-level Tsallis-Havrda-Charvt entropy and 2D histogram using PSO algorithms, " Pattern Recognition, vol. 92, pp. 107-118, 2019. doi: 10.1016/j.patcog.2019.03.011
[9]	L. Cao, P. Bao, and Z. K. Shi, "The strongest schema learning GA and its application to multilevel thresholding, " Image Vis. Comput., vol. 26, no. 5, pp. 716-724, 2008. doi: 10.1016/j.imavis.2007.08.007
[10]	P. D. Sathya and R. Kayalvizhi, "Optimal multilevel thresholding using bacterial foraging algorithm, "Expert Systems with Applications, vol. 38, no. 12, pp. 15549-15564, 2011. doi: 10.1016/j.eswa.2011.06.004
[11]	M. A. Bakhshali and M. Shamsi, " Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO), "J. Computational Science, vol. 5, no. 2, pp. 251-257, 2014. doi: 10.1016/j.jocs.2013.07.001
[12]	S. Pare, A. Kumar, and G. K. Singh, " Color multilevel thresholding using gray-level co-occurrence matrix and differential evolution algorithm, "in Proc. IEEE Int. Conf. Communication and Signal Processing (ICCSP), 2017, pp. 96-100.
[13]	L. Xu, H. Jia, C. Lang, X. Peng, and K. Sun, " A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution, "IEEE Access, vol. 7, pp. 19502-19538, 2019. doi: 10.1109/ACCESS.2019.2896673
[14]	S. Pare, A. K. Bhandari, A. Kumar, and G. K. Singh, " A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm, "Computers & Electrical Engineering, vol. 70, pp. 476-495, 2018. doi: 10.1016/j.compeleceng.2017.08.008
[15]	M. H. Horng and R. J. Liou, " Multilevel minimum cross entropy threshold selection based on the firefly algorithm, "Expert Systems with Applications, vol. 38, no. 12, pp. 14805-14811, 2011. doi: 10.1016/j.eswa.2011.05.069
[16]	H. Gao, Z. Fu, C. M. Pun, H. Hu, and R. Lan, " A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm, "Computers & Electrical Engineering, vol. 70, pp. 931-938, 2018. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=404dd602a3e5d8d043d63e8ed5ff8011
[17]	A. K. Bhandari, A. Kumar, and G. K. Singh, " Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's Otsu and Tsalli's functions, Expert Systems with Applications, vol. 42, no. 3, pp. 1573-1601, 2015. doi: 10.1016/j.eswa.2014.09.049
[18]	H. Gao, Y. Shi, C. M. Pun, and S. Kwong, "An improved artificial bee colony algorithm with its application, " IEEE Trans. Industrial Informatics, vol. 15, no. 4, pp. 1853-1865, 2018. http://d.old.wanfangdata.com.cn/Periodical/dzjsyy201803022
[19]	S. Pare, A. K. Bhandari, A. Kumar, and G. K. Singh, "An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix, "Expert Systems with Applications, vol. 87, pp. 335-362, 2017. doi: 10.1016/j.eswa.2017.06.021
[20]	S. Kotte, R. K. Pullakura, and S. K. Injeti, " Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization, "Measurement, vol. 130, pp. 340-361, 2018. doi: 10.1016/j.measurement.2018.08.007
[21]	S. Pare, A. K. Bhandari, A. Kumar, and G. K. Singh, " Rényi's entropy and Bat algorithm based color image multilevel thresholding, "Machine Intelligence and Signal Analysis Springer, vol. 748, pp. 71-84, 2019. doi: 10.1007/978-981-13-0923-6_7
[22]	S. Pare, A. K. Bhandari, A. Kumar, and V. Bajaj, " Backtracking search algorithm for color image multilevel thresholding, "Signal, Image and Video Processing, vol. 12, no. 2, pp. 385-392, 2018. doi: 10.1007/s11760-017-1170-z
[23]	M. H. Horng, " A multilevel image thresholding using the honey bee mating optimization, "Applied Mathematics and Computation, vol. 215, no. 9, pp. 3302-3310, 2010. doi: 10.1016/j.amc.2009.10.018
[24]	M. H. Horng, " Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization, "Expert Systems with Applications, vol. 37, no. 6, pp. 4580-4592, 2010. doi: 10.1016/j.eswa.2009.12.050
[25]	D. Liu, X. Y. Zhang, and Y. J. Chen, " Monocrystalline silicon diameter detection image threshold segmentation method using multiobjective artificial fish swarm algorithm, "Acta Autom. Sinica, vol. 42, no. 3, pp. 431-442, 2016. doi: 10.16383/j.aas.2016.c150587
[26]	C. D. Fan, Y. J. Zhang, H. L. Ouyang, and L. Y. Xiao, " Improved Otsu method based on histogram oblique segmentation for segmentation of rotary kiln flame image, "Acta Autom. Sinica, vol. 40, no. 11, pp. 2480-2489, 2014. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdhxb201411010
[27]	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 Systems with Applications, vol. 41, no. 7, pp. 3538-3560, 2014. doi: 10.1016/j.eswa.2013.10.059
[28]	L. Ali. "Multilevel thresholding in image segmentation using swarm algorithms, " Advances in Intelligent Systems and Computing, vol. 2, pp. 201-210, 2015. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_957e5d1a3d9f8a37e8f74f100b1ea00c
[29]	C. Mala and M. Sridevi, "Multilevel threshold selection for image segmentation using soft computing techniques, " Soft Computing, pp. 1-18, 2015. doi: 10.1007/s00500-015-1677-6
[30]	H. Gao, W. Xu, J. Sun, and Y. Tang, "Multilevel thresholding for image segmentation through an improved quantum behaved particle swarm algorithm, " IEEE Trans. Instrum. Meas., vol. 59, pp. 934-946, 2010. doi: 10.1109/TIM.2009.2030931
[31]	Y. Li, X. Bai, L. Jiao, and Y. Xue, "Partitioned cooperative quantum behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation, " Applied Soft Computing, vol. 56, pp. 345-356, 2017. doi: 10.1016/j.asoc.2017.03.018
[32]	S. Pare, A. K. Bhandari, A. Kumar, G. K. Singh and S. Khare, "Satellite image segmentation based on different objective functions using genetic algorithm:a comparative study, " in Proc. IEEE Int. Conf. Digital Signal Processing (DSP), pp. 730-734, 2015. doi: 10.1109/ICDSP.2015.7251972
[33]	S. Sarkar, S. Das, and S. S. Chaudhuri, "A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution, " Pattern Recognition Letters, vol. 54, pp. 27-35, 2015. doi: 10.1016/j.patrec.2014.11.009
[34]	T. Kurban, P. Civicioglu, R. Kurban, and E. Besdok, "Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding, " Applied Soft Computing, vol. 23, pp. 128-143, 2014. doi: 10.1016/j.asoc.2014.05.037
[35]	A. K. Bhandari, A. Kumar, and G. K. Singh, "Tsalli's entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms, "Expert Systems with Applications, vol. 42, no. 22, pp. 8707-8730, 2015. doi: 10.1016/j.eswa.2015.07.025
[36]	A. K. Bhandari, A. Kumar, S. Chaudhary, and G. K. Singh, " A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms, "Expert Systems with Applications, vol. 63, pp. 112-133, 2016. doi: 10.1016/j.eswa.2016.06.044
[37]	S. Patra, R. Gautam, and A. Singla, " A novel context sensitive multilevel thresholding for image segmentation, "Applied Soft Computing, vol. 23, pp. 122-127, 2014. doi: 10.1016/j.asoc.2014.06.016
[38]	A. Singla and S. Patra, " A context sensitive thresholding technique for automatic image segmentation, "Computational Intelligence in Data Mining, Springer India, vol. 2, pp. 19-25, 2015. doi: 10.1007/978-81-322-2208-8_3
[39]	D. Y. Huang and C. H. Wang, " Optimal multi-level thresholding using a two-stage Otsu optimization approach, "Pattern Recognit. Lett., vol. 30, no. 3, pp. 275-284, 2009. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b77ce117e5bf14d7e7e5e5bbed14b94c
[40]	F. Van Den Bergh and A. P. Engelbrecht, " A cooperative approach to particle swarm optimization, "IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 225-238, 2004. doi: 10.1109/TEVC.2004.826069
[41]	B. Akay, " A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding, "Applied Soft Computing, vol. 13, no. 6, pp. 3066-3091, 2013. doi: 10.1016/j.asoc.2012.03.072
[42]	A. H. Gandomi, X. S. Yang, S. Talatahari, and A. H. Alavi, " Firefly algorithm with chaos, "Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89-98, 2013. doi: 10.1016/j.cnsns.2012.06.009
[43]	W. Gong and Z. Cai, " Differential evolution with ranking-based mutation operators, "IEEE Trans. Cybernetics, vo. 43, no. 6, pp. 2066-2081, 2013. doi: 10.1109/TCYB.2013.2239988
[44]	S. Sarkar and S. Das, " Multilevel image thresholding based on 2D histogram and maximum Tsalli's entropy-a differential evolution approach, IEEE Trans. Image Processing, vol. 22, no. 12, pp. 4788-4797, 2013. doi: 10.1109/TIP.2013.2277832
[45]	K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control, " IEEE Trans. Control Systems Magazine, vol. 22, no. 3, pp. 52-67, 2002. doi: 10.1109/MCS.2002.1004010
[46]	Z. Bayraktar, J. P. Turpin, and D. H. Werner, "Nature-inspired optimization of high-impedance metasurfaces with ultrasmall interwoven unit cells, " IEEE Antennas and Wireless Propagation Letters, vol. 10, pp. 1563-1566, 2011. doi: 10.1109/LAWP.2011.2178224

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A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

doi: 10.1109/JAS.2017.7510697

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