2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 1 , PP: 68-76, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Abdulaziz Saleh Alraddadi 1 , Essam O. Abdel-Rahman 2 *
Doi: https://doi.org/10.54216/FPA.200106
The current study introduces a trainable object detection model that can be taught to detect an object of a given class within an unconstrained scene. The researchers of the current study use this advanced system in the detection of Relics images, which involves a calculation of Local 3bit Binary Patterns (3bit-LBP). The key highlights of the current work include the integration and analyses of the utilization of the Multi-Support Vector Machine Classification (MSVMC) and Integral image computation analysis. The experimental outcomes of the current study indicate that the method of 3bit-LBP is superior to other methods in accuracy and stability, especially when images of different illumination and object rotation were tested. The researchers further conducted a comparative performance evaluation showing that the presented system gives better detection rates as compared to the conventional strategies, revealing the efficiency in real-world applications. Finally, it is important to note that the implications of the results can be applied to uses beyond just relic detection. To conclude, the current work advances the knowledge of how to improve the functionality of object recognition algorithms further in the context of image recognition systems.
Image recognition , Multi-Support Vector Machine Classification , Local Binary Patterns , Binary Classifier
[1] R. Szeliski, Computer Vision: Algorithms and Applications, Springer Nature, 2022.
[2] Y. Li, "Research and application of deep learning in image recognition," in 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 2022.
[3] P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea, "Machine recognition of human activities: A survey," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1473-1488, 2008.
[4] I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, "Past, present, and future of face recognition: A review," Electronics, vol. 9, no. 8, p. 1188, 2020.
[5] G. Rose, Visual Methodologies: An Introduction to Researching with Visual Materials, 2022.
[6] Z.-Q. Zhao, P. Zheng, S.-T. Xu, and X. Wu, "Object detection with deep learning: A review," IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 11, pp. 3212-3232, 2019.
[7] A. Pramod, H. S. Naicker, and A. K. Tyagi, "Machine learning and deep learning: Open issues and future research directions for the next 10 years," in Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications, 2021, pp. 463-490.
[8] S. Sengupta, S. Basak, P. Saikia, S. Paul, V. Tsalavoutis, F. Atiah, V. Ravi, and A. Peters, "A review of deep learning with special emphasis on architectures, applications and recent trends," Knowl.-Based Syst., vol. 194, p. 105596, 2020.
[9] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-end object detection with transformers," in European Conference on Computer Vision, 2020.
[10] A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, and A. Kolesnikov, "The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale," Int. J. Comput. Vis., vol. 128, no. 7, pp. 1956-1981, 2020.
[11] Y. Xiao, Z. Tian, J. Yu, Y. Zhang, S. Liu, S. Du, and X. Lan, "A review of object detection based on deep learning," Multimedia Tools Appl., vol. 79, pp. 23729-23791, 2020.
[12] S. Li, C. Zheng, R. Wang, Y. Huo, W. Zheng, H. Lin, and H. Bao, "Multi-resolution terrain rendering using summed-area tables," Computers Graphics, vol. 95, pp. 130-140, 2021.
[13] M.-S. Șerbănescu, "Fractal dimension box-counting algorithm optimization through integral images," in 7th International Conference on Advancements of Medicine and Health Care through Technology: Proceedings of MEDITECH-2020, 2022, pp. 13-15.
[14] S. Karanwal and M. Diwakar, "EDRM-LBP: Effective directional radial median local binary pattern for face recognition," Int. J. Embedded Syst., vol. 15, no. 6, pp. 475-492, 2022.
[15] Q. Wang and L. Li, "Museum relic image detection and recognition based on deep learning," Computational Intelligence Neurosci., vol. 2022, p. 9670191, 2022.
[16] M. Singla and K. Shukla, "Robust statistics-based support vector machine and its variants: A survey," Neural Comput. Appl., vol. 32, no. 15, pp. 11173-11194, 2020.
[17] S. Ghosh, A. Dasgupta, and A. Swetapadma, "A study on support vector machine based linear and non-linear pattern classification," in 2019 International Conference on Intelligent Sustainable Systems (ICISS), 2019.
[18] J. Nalepa and M. Kawulok, "Selecting training sets for support vector machines: A review," Artif. Intell. Rev., vol. 52, no. 2, pp. 857-900, 2019.
[19] S. Saha, "An empirical comparison of linear and non-linear classification using support vector machines," Int. J. Comput. Sci. Eng., vol. 11, pp. 120-126, 2023.
[20] F. A. Fardo and P. S. S. Rodrigues, "A sparse local binary pattern extraction algorithm applied to event sensor data for object classification," Pattern Recognit., vol. 146, p. 110004, 2024.
[21] B. M. Rashed and N. Popescu, "Critical analysis of the current medical image-based processing techniques for automatic disease evaluation: Systematic literature review," Sensors, vol. 22, no. 18, p. 7065, 2022.
[22] R. M. Anwer, F. S. Khan, J. Van De Weijer, M. Molinier, and J. Laaksonen, "Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification," ISPRS J. Photogramm. Remote Sens., vol. 138, pp. 74-85, 2018.
[23] C. Ranjeeth Kumar and M. Kalaiarasu, "Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers," J. Intell. Fuzzy Syst., vol. 44, no. 3, pp. 3943-3958, 2023.
[24] W. A. N. Jasim and R. J. Mohammed, "A survey on segmentation techniques for image processing," Iraqi J. Electr. Electron. Eng., vol. 17, no. 2, 2021.
[25] W. Burger and M. J. Burge, Digital Image Processing: An Algorithmic Introduction, Springer Nature, 2022.
[26] N. Elazab, W. Gab Allah, and M. Elmogy, "Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features," BMC Med. Imaging, vol. 24, no. 1, p. 177, 2024.
[27] J. Weston and C. Watkins, "Support vector machines for multi-class pattern recognition," in ESANN, 1999.
