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Grey wolf optimizer-based learning automata for solving block matching problem

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Abstract

Block matching problem is of great importance, and it is the basic element of many computer vision systems such as video compression, object tracking, motion analysis, and traffic control. This paper proposes a novel grey wolf optimizer (GWO) algorithm based on learning automata (LA) to solve block matching problem for motion estimation. Two main contributions are presented in this paper. Firstly, for improving the exploration and exploitation abilities of the GWO technique, an enhanced GWO method based on LA algorithm is proposed. LA is integrated in the GWO to learn the objective function and decide whether it is an unimodal or multimodal function. Unimodal function needs a good exploitation of promising area in the search space. However, multimodal function requires high exploration ability. The classification obtained using LA is then used to create new solutions in the appropriate areas. In the creation phase, two equations are used. The first one is based on a Gaussian distribution, to enrich the exploitation for the unimodal function, and the second is based on a random distribution to support the exploration in multimodal function. The second contribution of this paper consists of applying our enhanced GWO algorithm in block matching problem. The proposed algorithm is validated on two phases. Firstly, we evaluate our enhanced GWO algorithm on eight well-known benchmark functions. The reported results show that the enhanced GWO algorithm has the potential to improve the optimization abilities of the conventional GWOs. Then, the proposed enhanced GWO algorithm-based block matching is tested on six video sequences and compared with several state-of-the-art methods. Simulation results show the effectiveness of the proposed BM algorithm and prove the applicability of our enhanced GWO to real-world optimization problem.

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References

  1. Fortun, D., Bouthemy, P., Kervrann, C.: Optical flow modeling and computation: a survey. Comput. Vis. Image Underst. 134, 1–21 (2015)

    Article  Google Scholar 

  2. Jain, J., Jain, A.: Displacement measurement and its application in interframe image coding. IEEE Trans. Commun. 29(12), 1799–1808 (1981)

    Article  Google Scholar 

  3. Jong, H.M., Chen, L.G., Chiueh, T.D.: Accuracy improvement and cost reduction of 3-step search block matching algorithm for video coding. IEEE Trans. Circuits Syst. Video Technol. 4(1), 88–90 (1994)

    Article  Google Scholar 

  4. Po, L.M., Ma, W.C.: A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 6(3), 313–317 (1996)

    Article  Google Scholar 

  5. LiI, R., Zeng, B., Liou, M.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 4(4), 438–442 (1994)

    Article  Google Scholar 

  6. Lu, J., Liou, M.L.: A simple and efficient search algorithm for block-matching motion estimation. IEEE Trans. Circuits Syst. Video Technol. 7(2), 429–433 (1997)

    Article  Google Scholar 

  7. Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block matching motion estimation. In: International Conference on Information, Communications and Signal Processing, ICICS, IEEE’97, pp. 292–296, (1997)

  8. Ahmed, Z., Hussain, A.J., Al-Jumeily, D.: Mean predictive block matching (MPBM) for fast block-matching motion estimation. In: 3rd European Workshop on Visual Information Processing. IEEE’2011, pp. 67–72

  9. Sahu, S.K., Shukla, D.: A new approach of block matching motion estimation algorithm for H. 264/AVC Video Codec. i-Manager’s J. Pattern Recognit. 4(2), 10 (2017)

    Article  Google Scholar 

  10. Long, W., Jiao, J., Liang, X., et al.: An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng. Appl. Artif. Intell. 68, 63–80 (2018)

    Article  Google Scholar 

  11. Gupta, S., Deep, K.: A novel random walk grey wolf optimizer. Swarm Evol. Comput. 44, 101–112 (2019)

    Article  Google Scholar 

  12. Long, W., Jiao, J., Liang, X., et al.: Inspired grey wolf optimizer for solving large-scale function optimization problems. Appl. Math. Model. 60, 112–126 (2018)

    Article  MathSciNet  Google Scholar 

  13. Lu, C., Gao, L., Yi, J., et al.: Grey wolf optimizer with cellular topological structure. Expert Syst. Appl. 107, 89–114 (2018)

    Article  Google Scholar 

  14. Kohli, M., Arora, S.: Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Des. Eng. 5(4), 458–472 (2018)

    Google Scholar 

  15. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  16. Narendra, K.S., Thathachar, M.A.: Learning Automata: An Introduction. Courier Corporation, Chelmsford (2012)

    Google Scholar 

  17. Crepinsek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)

    Article  Google Scholar 

  18. Cuevas, E., Zaldivar, D., Pérez-cisneros, M., et al.: Block-matching algorithm based on differential evolution for motion estimation. Eng. Appl. Artif. Intell. 26(1), 488–498 (2013)

    Article  Google Scholar 

  19. Nie, Y., Ma, K.-K.: Adaptive rood pattern search for fast block-matching motion estimation. IEEE Trans. Image Process. 11(12), 1442–1449 (2002)

    Article  Google Scholar 

  20. CaiAI, J., Pan, W.D.: On fast and accurate block-based motion estimation algorithms using particle swarm optimization. Inf. Sci. 197, 53–64 (2012)

    Article  Google Scholar 

  21. Ren, R., Shi, Y., Zheng, B. et al.: A Fast Block Matching Algorithm for Video Motion Estimation Based on Particle Swarm Optimization and Motion Prejudgment. arXiv preprint arXiv:cs/0609131, (2006)

  22. Huang, H.-C., Hung, Y.-P., Hwang, W.-L.: Adaptive early jump-out technique for fast motion estimation in video coding. In: Proceedings of the 13th International Conference on IEEE Pattern Recognition, (1996)

  23. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on IEEE Micro Machine and Human Science. MHS’95, (1995)

  24. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)

    Article  MathSciNet  Google Scholar 

  25. Mirjalili, S., et al.: Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)

    Article  Google Scholar 

  26. Pierezan, J., Coelho, L.D.S.: Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: International IEEE’2018 Congress on Evolutionary Computation (CEC), pp. 1–8, (2018)

  27. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  28. Wang, G.G.: Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet. Comput. 10(2), 151–164 (2018)

    Article  Google Scholar 

  29. Damerchilu, B., Norouzzadeh, M.S., Meybodi, M.R.: Motion estimation using learning automata. Mach. Vis. Appl. 27(7), 1047–1061 (2016)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Biskra, Biskra, Algeria

    Abir Betka, Nadjiba Terki & Abida Toumi

  2. Genie Electric Department, University of M’sila, M’sila, Algeria

    Habiba Dahmani

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  1. Abir Betka
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  2. Nadjiba Terki
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  3. Abida Toumi
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  4. Habiba Dahmani
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Betka, A., Terki, N., Toumi, A. et al. Grey wolf optimizer-based learning automata for solving block matching problem. SIViP 14, 285–293 (2020). https://doi.org/10.1007/s11760-019-01554-w

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