ICTACT Journal on Image and Video Processing

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Application of Teaching Learning Based Optimization in Multilevel Image Thresholding


Affiliations
1 Department of Electrical Engineering, Annamalai University, India
     

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This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific, parameterless algorithm that does not require any algorithm-specific parameters to be controlled by maximizing the Kapur entropy of various classes for image thresholding. The proposed model is compared with recent algorithms to threshold the seven standard benchmark and three test images. The simulation results have higher fitness function values even with the increase of the threshold number with less computation time. The Jaccard measure values are close to 0.99.

Keywords

Kapur’s Entropy, Multilevel Thresholding, Teaching Learning Based Optimization.
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  • S. Dey, S. Bhattacharyya and U. Maulik, “Quantum Behaved Multi-Objective PSO and ACO Optimization for Multi-Level Thresholding”, Proceedings of International Conference on Computational Intelligence and Communication Networks, pp. 242-246, 2014.

  • A.K. Bhandari, A. Kumar and G.K. Singh, “Tsallis Entropy based Multilevel Thresholding for Colored Satellite Image Segmentation using Evolutionary Algorithms”, Expert Systems with Applications, Vol. 42, No. 22, pp. 8707-8730, 2015.

  • N. Otsu, “A Threshold Selection method from Gray-Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.

  • J.N. Kapur, P.K. Sahoo and A.K. Wong, “A New Method for Gray-Level Picture Thresholding using the Entropy of the Histogram”, Computer Vision, Graphics, and Image Processing, Vol. 29, No. 3, pp. 273-285, 1985.

  • L.K. Huang and M.J.J. Wang, “Image Thresholding by Minimizing the Measures of Fuzziness”, Pattern Recognition, Vol. 28, No. 1, pp. 41-51, 1995.

  • Y. Qiao, Q. Hu, G. Qian, S. Luo and W.L. Nowinski, “Thresholding based on Variance and Intensity Contrast”, Pattern Recognition, Vol. 40, No. 2, pp. 596-608, 2007.

  • X. Li, Z. Zhao and H.D. Cheng, “Fuzzy Entropy Threshold Approach to Breast Cancer Detection”, Information Sciences Applications, Vol. 4, No. 1, pp. 49-56, 1995.

  • C.H. Li and P.K.S. Tam, “An Iterative Algorithm for Minimum Cross Entropy Thresholding”, Pattern Recognition Letters, Vol. 19, No. 8, pp. 771-776, 1998.

  • K. Li and Z. Tan, “An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding”, IEEE Access, Vol. 7, pp. 165571-165582, 2019.

  • J. Kittler and J. Illingworth, “Minimum Error Thresholding”, Pattern Recognition, Vol. 19, No. 1, pp. 41-47, 1986.

  • S. Zarezadeh and M. Asadi, “Results on Residual Renyi Entropy of Order Statistics and Record Values”, Information Sciences, Vol. 180, No. 21, pp. 4195-4206, 2010.

  • S. Ouadfel and A. Taleb-Ahmed, “Social Spiders Optimization and Flower Pollination Algorithm for Multilevel Image Thresholding: A Performance Study”, Expert Systems with Applications, Vol. 55, pp. 566-584, 2016.

  • S.S. Pal, S. Kumar, K. Kashyap, Y. Choudhary and M. Bhattacharya, “Multi-Level Thresholding Segmentation Approach based on Spider Monkey Optimization Algorithm”, Proceedings of International Conference on Computer and Communication Technologies, pp. 273-287, 2016.

  • M.A. El Aziz, A.A. Ewees and A.E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation”, Expert Systems with Applications, Vol. 83, pp. 242-256, 2017.

  • A. K. M. Khairuzzaman, and S. Chaudhury, “Multilevel Thresholding using Grey Wolf Optimizer for Image Segmentation”, Expert Systems with Applications, Vol. 86, pp. 64-76, 2017.

  • 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.

  • K.B. Resma and M.S. Nair, “Multilevel Thresholding for Image Segmentation using Krill Herd Optimization Algorithm”, Journal of King Saud University-Computer and Information Sciences, Vol. 12, No, 3, pp. 1-13, 2018.

  • M. Ahmadi, K. Kazemi, A. Aarabi, T. Niknam and M.S. Helfroush, “Image Segmentation using Multilevel Thresholding based on Modified Bird Mating Optimization”, Multimedia Tools and Applications, Vol. 78, No. 16, pp. 23003-23027, 2019.

  • A. Sharma, R. Chaturvedi, S. Kumar and U.K. Dwivedi, “Multi-Level Image Thresholding based on Kapur and Tsallis Entropy using Firefly Algorithm”, Journal of Interdisciplinary Mathematics, Vol. 23, No. 2, pp. 563-571, 2020.

  • M. Abdel-Basset, V. Chang and R. Mohamed, “A Novel Equilibrium Optimization Algorithm for Multi-Thresholding Image Segmentation Problems”, Neural Computing and Applications, Vol. 34, No. 1, pp.1-34, 2020.

  • R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-Learning-Based Optimization: An Optimization Method for Continuous Non-Linear Large-Scale Problems”, Information Sciences, Vol. 183, No. 1, pp. 1-15, 2012.

  • R.V. Rao, V.J. Savsani and J. Balic, “Teaching-Learning-Based Optimization Algorithm for Unconstrained and Constrained Real-Parameter Optimization Problems”, Engineering Optimization, Vol. 44, No. 12, pp. 1447-1462, 2012.

  • R.V. Rao and V.D. Kalyankar, “Parameter Optimization of Machining Processes using a New Optimization Algorithm”, Materials and Manufacturing Processes, Vol. 27, No. 9, pp. 978-985, 2012.

  • M.A. Elaziz, A.A. Ewees and A.E. Hassanien, “Hybrid Swarms Optimization based Image Segmentation”, Proceedings International Conference on Hybrid Soft Computing for Image Segmentation, pp. 1-21, 2016.


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  • Application of Teaching Learning Based Optimization in Multilevel Image Thresholding

Abstract Views: 264  |  PDF Views: 1

Authors

S. Anbazhagan
Department of Electrical Engineering, Annamalai University, India

Abstract


This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific, parameterless algorithm that does not require any algorithm-specific parameters to be controlled by maximizing the Kapur entropy of various classes for image thresholding. The proposed model is compared with recent algorithms to threshold the seven standard benchmark and three test images. The simulation results have higher fitness function values even with the increase of the threshold number with less computation time. The Jaccard measure values are close to 0.99.

Keywords


Kapur’s Entropy, Multilevel Thresholding, Teaching Learning Based Optimization.

References