REDUCING COMPUTATIONAL COSTS BY COMPRESSING THE STRUCTURAL DESCRIPTION IN IMAGE CLASSIFICATION METHODS
Article Sidebar
Main Article Content
Abstract
The research of the article is focused on ways to reduce the amount of analyzed data when applying image classification methods in computer vision systems. The aim of this work is to develop approaches to reduce the dimensionality of the vector description of the etalon base using metric granulation, which reduces computational costs and speeds up the classification process while maintaining a sufficient level of accuracy. Methods used: keypoint descriptors, metric data granulation apparatus, image classification and processing theory, data structures, software modeling. Results: the formalism of granular representation was developed; experimental modeling was carried out using five-level granulation, which reduced the time spent tenfold while maintaining high classification accuracy. In the comparative aspect, we studied ways to reduce the volume of vector descriptions based on data discarding, and researched the effect of the granularity level on the accuracy and classification time. The practical significance of the work is to improve the performance of image classification structural methods by implementing granularity and data discarding schemes, which provides much faster data processing without significant loss of classification performance.
Article Details
References
Tymchyshyn, R., Volkov, O., Gospodarchuk, O. and Bogachuk, Yu. (2018), “Modern Approaches to Computer Vision”, Control systems and computers, vol. 6, pp. 46–73, doi: https://doi.org/10.15407/usim.2018.06.046
Gorokhovatskyi, V., Tvoroshenko, I., Yakovleva, O., Hudáková, M. and Gorokhovatskyi, O. (2024), “Application a committee of Kohonen neural networks to training of image classifier based on description of descriptors set,” IEEE Access, vol. 12, pp. 73376–73385, doi: https://doi.org/10.1109/ACCESS.2024.3404371
Putyatin, E. P. and Averin, S. I. (1990), Image processing in robotics, Mashinostroeniye, 1990, 320 p., available at: https://scholar.google.com.ua/citations?view_op=view_citation&hl=uk&user=dftWDBoAAAAJ&citation_for_view=dftWDBoAAAAJ:isC4tDSrTZIC
Gadetska, S. V., Gorokhovatskyi, V. O., Stiahlyk, N. I. and Vlasenko, N. V. (2022), “Statistical data analysis tools in image classification methods based on the description as a set of binary descriptors of key points”, Radio Electronics, Computer Science, Control, no. 4, pp. 58–68, Jan. 2022, doi: https://doi.org/10.15588/1607-3274-2021-4-6
Lowe, D. G. (2004), “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, vol. 60 (2), doi: https://doi.org/10.1023/B:VISI.0000029664.99615.94
Rublee, E., Rabaud, V., Konolige, K. and Bradski, G. (2011), “ORB: An efficient alternative to SIFT or SURF”, Proc. Int. Conf. Comput. Vis., Nov. 2011, Barcelona, Spain, pp. 2564–2571, doi: https://doi.org/10.1109/ICCV.2011.6126544
Crowley, J. and Riff, O. (2003), “Fast computation of scale normalized Gaussian receptive fields”, Proc. Scale-Space'03, Isle of Skye, Scotland, Springer Lecture Notes in Computer Science, 2695, doi: https://doi.org/10.1007/3-540-44935-3_41
Li, H., Xie, F., Zhou, J. and Liu, J. (2024), “Object Detection, Segmentation and Categorization in Artificial Intelligence”, Electronics, vol. 13, no. 2650, doi: https://doi.org/10.3390/electronics13132650
Daradkeh, Y.I., Gorokhovatskyi, V., Tvoroshenko, I. and Zeghid, M. (2022), “Cluster representation of the structural description of images for effective classification”, Computers, Materials & Continua, vol. 73 (3), pp. 6069–6084, doi: https://doi.org/10.32604/cmc.2022.030254
Baldini, L., Martino, A. and Rizzi, A. (2019), “Stochastic information granules extraction for graph embedding and classification”, Proc. of the 11th Int. Joint Conf. on Comp. Intelligence, doi: https://doi.org/10.5220/0008149403910402
Petrovska, I., Kuchuk, H. and Mozhaiev, M. (2022), “Features of the distribution of computing resources in cloud systems”, 2022 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2022 - Conference Proceedings, 03-07 October 2022, Code 183771, doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916459
Gorokhovatskyi, V., Gadetska, S. and Stiahlyk, N. (2023), “Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance”, International Journal of Computing, vol. 22(4), pp. 485–492, doi: https://doi.org/10.47839/ijc.22.4.3355
Zadeh, L. A. (1997), “Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic”, Fuzzy sets and systems, vol. 90 (2), pp. 111–127, doi: https://doi.org/10.1016/S0165-0114(97)00077-8
Fang, Y. and Liu, L. (2022), “Angular quantization online hashing for image retrieval”, IEEE Access, vol. 10, pp. 72577–72589, doi: https://doi.org/10.1109/ACCESS.2021.3095367
Flach, P. (2012), “Model ensembles”, The Art and Science of Algorithms That Make Sense of Data, New York, NY, USA: Cambridge Univ. Press, pp. 330–342, doi: https://doi.org/10.1017/CBO9780511973000
Aggarwal, C.C. (2023), Neural networks and deep learning, Textbook, Springer International Publishing, 529 p., doi: https://doi.org/10.1007/978-3-031-29642-0
Xiong, H. and Li, Z. (2014), Data Clustering: Algorithms and Application, 1st ed., CRC Press, Boca Raton, 652 p., doi: https://doi.org/10.1201/9781315373515
Zhang X., Yu, F. X., Karaman, S. and Chang, S.-F. (2017), “Learning discriminative and transformation covariant local feature detectors”, IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, Honolulu, HI, USA, pp. 4923–4931, doi: https://doi.org/10.1109/CVPR.2017.523
Butenkov, S. (2004), “Granular Computing in Image Processing and Understanding”, Proc. of IASTED International Conf. On AI and Applications «AIA 2004», Innsbruk (Austria), February 10-14, 2004, doi: https://doi.org/10.1016/j.procs.2017.01.111
Vorobel, R. A. (2012), Logarithmic image processing, Kyiv, Naukova Dumka, 232 р., available at: https://scholar.google.com/citations?view_op=view_citation&hl=uk&user=vEtLugEAAAAJ&citation_for_view=vEtLugEAAAAJ:qjMakFHDy7sC
Daradkeh, Y.I., Gorokhovatskyi, V., Tvoroshenko, I., and Zeghid, M. (2024), “Improving the effectiveness of image classification structural methods by compressing the description according to the information content criterion”, Computers, Materials & Continua, vol. 80, no. 2, pp. 3085–3106, doi: https://doi.org/10.32604/cmc.2024.051709
Yakovleva, О., Kovtunenko, A., Liubchenko, V., Honcharenko, V. and Kobylin, O. (2023), “Face Detection for Video Surveillance-based Security System (COLINS-2023)”, CEUR Workshop Proceedings, vol. 3403, pp. 69–86, available at: https://www.sytoss.com/blog/face-detection-for-video-surveillance-based-security-system
Ullah, Z., Qi, L., Pires, E.J.S., Reis, A. and Nunes, R.R. (2024), “A systematic review of computer vision techniques for quality control in end-of-line visual inspection of antenna parts. Computers”, Materials & Continua, vol. 80(2), pp. 2387–2421, doi: https://doi.org/10.32604/cmc.2024.047572
Gorokhovatskyi, O., and Yakovleva, O. (2024), “Medoids as a packing of orb image descriptors”, Advanced Information Systems, vol. 8(2), pp. 5–11, doi: https://doi.org/10.20998/2522-9052.2024.2.01
Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, vol. 8(1), pp. 66-69, doi: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019
Svyrydov, A., Kuchuk, H. and Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593–597, doi: https://doi.org/10.1109/DESSERT.2018.8409201
Gorokhovatskyi, V. and Vlasenko, N. (2021), “The image description reduction in the set of descriptors on informativeness metric criteria base”, Advanced Information Systems, vol. 5 (4), pp. 10–16, doi: https://doi.org/10.20998/2522-9052.2021.4.02
(2024), OpenCV, available at: https://docs.opencv.org/