SEGMENTATION OF A BIDDEN SIGNAL, WHICH REPRESENTS A PICTURE OF ORDERED GOODS BY THE CONSUMER

Володимир Олександрович Патрушев, Ольга Ігорівна Патрушева

Abstract


The subject of study in this article is the means of segmentation of the image of the ordered goods by the consumer online store. The goal is to determine the means of segmentation of the image of a two-dimensional signal. Objectives: analyze existing methods of image segmentation, select metaheuristic clustering with an interactive task of the number of clusters, conduct research. Methods used: segmentation of a two-dimensional signal, which is an image of a product ordered by a consumer in an online store. A meta-heuristic clustering method was implemented with an interactive assignment of the number of clusters. The method is based on the optimization of particle swarm (PSO) and annealing simulation (SA), an adaptive optimization of particle swarm (APSO), which underlies the image segmentation, is proposed. The following results were obtained. The use of simulated annealing in the proposed adaptive optimization of a particle swarm provides: control of the rate of convergence of a given metaheuristic method; research in the early stages of the entire search space, and in the final stages - the focus of the search. To determine the effectiveness of the proposed method, studies have been conducted that prove that the mean square error does not exceed 0.05, which in turn proves the effectiveness of the chosen method in image segmentation. Conclusions. The scientific novelty lies in the fact that to solve the problem of determining the method of image preprocessing, a clustering method with a given number of clusters was used, namely a metaheuristic method based on optimizing the particle swarm (PSO) and simulating annealing (SA) using adaptive particle swarm optimization (APSO), which underlies the image segmentation. The use of simulated annealing in the proposed adaptive optimization of a swarm of particles provides: control of the rate of convergence of a given meta-heuristic method and research in the early stages of the entire search space, and in the final stages the direction of the search. As a result of a numerical study, it was found that the mean square error does not exceed 0.05.


Keywords


image; segmentation; two-dimensional signal; clustering; adaptive optimization of swarm particles; mean square error

References


Iliev, V. P., Ilyeva, S. On problems of graph clustering. Vestnik Omskogo universiteta, 2016, no.2, pp. 16-18. (In Russian).

Neysky, I. M. Classification and comparison of clustering methods. Available at: http://it-claim.ru/Persons/Neyskiy/Article2_Neiskiy.pdf (аccessed 12.12.2018) (In Russian).

Tsitsiashvili, G. Sh., Osipova, M., Losev, A. S. Graph Clustering Algorithms. VSU Bulletin. Series: Physics. Mathematics, 2016, no. 1, pp.145-149. (In Russian).

Klyshinsky, E. S. The clustering method based on the analysis of point density. New information technologies in automated systems, 2014, pp. 150-159. (In Russian).

Ren, S., He, K., Girshick, R., Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Available at: https://arxiv. org/abs/1506.01497.html (аccessed 12.12.2018)

Dai, J., He, K., Sun, J. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. Available at: https://arxiv.org/abs/1503.01640.html (аccessed 12.12.2018)

David, G. Lowe Distinctive Image Features from Scale-Invariant Keypoints. Available at: http://link.springer.com/article/10.1023/B:VISI.0000029664.99615.94 (аccessed 12.12.2018)

Saemi, B., Hosseinabadi, A. A. R., Kardgar, M., Balas, V. E., Ebadi, H. Nature Inspired Partitioning Clustering Algorithms: A Review and Analysis. Proceedings of the 7th International Workshop Soft Computing Applications, 2016, pp. 96-116.

Azami, Hamed., Hassanpour, Hamid., Escudero, Javier., Sanei, Saeid. An intelligent approach for variable size segmentation of non-stationary signals. Journal of Advanced Research, 2014, vol. 6(5), pp. 687–698. Doi: 10.1016/j.jare.2014.03.004.

Wang, Wuli., Duan, Liming., Wang, Yong. Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm. Journal of Electrical and Computer Engineering, 2017, vol. 2017, Article ID 1735176. 12 p. Doi: 10.1155/2017/1735176.

Wang, Xi-Huai., Li, Jun-Jun. Hybrid particle swarm optimization with simulated annealing. Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 26-29 Aug. 2004. DOI: 10.1109/ICMLC.2004.1382205.

Yan, Z. C., Luo, Y. S. A Particle Swarm Optimization Algorithm Based on Simulated Annealing. Materials Science, Computer and Information Technology, 2014, vols. 989-994, pp. P. 2301-2305. Doi: 0.4028/www.scientific.net/AMR.989-994.2301.

Javidrad, F., Nazari, M. A new hybrid particle swarm and simulated annealing stochastic optimization method. Applied Soft Computing, 2017, vol. 60, iss. C, pp. 634-654. Doi: 10.1016/j.asoc.2017.07.023.

Tang, D., Dai, M., Salido, M. A., Giret, A. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization Computers in Industr, 2016, vol. 81, pp. 82–95. DOI: 10.1016/j.compind.2015.10.001.




DOI: https://doi.org/10.32620/reks.2019.1.04

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