Content-Based Image Retrieval using Support Vector Machine and Clustering
الموضوعات : International Journal of Data Envelopment Analysis
محمدمهدی علیان نژادی
1
,
Ahmad Kamandi
2
,
Fatemeh Ahangar Darabi
3
,
Hesamoddin Pourrostami
4
,
سید محمدرضا هاشمی
5
1 - گروه علوم کامپیوتر، دانشگاه علم و فناوری مازندران، بهشهر، ایران
2 - Department of Mathematics, University of Science and Technology of Mazandaran, Iran
3 - Department of Mathematics, University of Science and Technology of Mazandaran, Behshahr, Iran
4 - Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
5 - گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی شاهرود، شاهرود، ایران.
الکلمات المفتاحية: Content-based image retrieval, clustering, Support vector machines, classification, k-means, ResNet50,
ملخص المقالة :
Accurate content-based image retrieval (CBIR) is critical for applications ranging from large-scale digital libraries to real-time mobile search, yet many high-precision methods suffer from prohibitive query-time costs as database sizes grow. To address this challenge, we introduce a scalable two-stage retrieval framework that balances accuracy with efficiency. First, we extract deep convolutional features from all images using a pre-trained ResNet50 model and partition the feature space into semantically coherent clusters via K-means. During retrieval, an incoming query image undergoes feature extraction and is quickly assigned to the most relevant cluster using a directed acyclic graph support vector machine classifier. Within the selected cluster, we perform fine-grained similarity ranking to produce the final set of retrieved images. We validate our approach on the Corel-10k dataset, demonstrating that our method achieves a recall of 0.91 and a precision of 0.93, while reducing average query time by 45% compared to a non-clustered baseline. Ablation studies reveal the impact of cluster granularity and classifier choice on retrieval performance. The proposed framework offers an effective solution for large-scale CBIR, maintaining high retrieval precision with substantially lower latency.
[1] A. Oussama, B. Khaldi, and M. L. Kherfi, "A fast weighted multi-view Bayesian learning scheme with deep learning for text-based image retrieval from unlabeled galleries," Multimedia Tools and Applications, vol. 82, no. 7, pp. 10795–10812, 2023.
[2] I. M. Hameed, S. H. Abdulhussain, and B. M. Mahmmod, "Content-based image retrieval: A review of recent trends," Cogent Engineering, vol. 8, no. 1, p. 1927469, 2021.
[3] J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, "Image matching from handcrafted to deep features: A survey," International Journal of Computer Vision, vol. 129, no. 1, pp. 23–79, 2021.
[4] M. V. Lande and S. Ridhorkar, "A comprehensive survey on content-based image retrieval using machine learning," Proceedings of Data Analytics and Management: ICDAM 2021, Volume 2, pp. 165–179, 2021.
[5] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
[6] H. Liu, B. Li, X. Lv, and Y. Huang, "Image retrieval using fused deep convolutional features," Procedia Computer Science, vol. 107, pp. 749–754, 2017.
[7] Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, "A survey of content-based image retrieval with high-level semantics," Pattern recognition, vol. 40, no. 1, pp. 262–282, 2007.
[8] K. Crammer and Y. Singer, "On the algorithmic implementation of multiclass kernel-based vector machines," Journal of machine learning research, vol. 2, no. Dec, pp. 265–292, 2001.
[9] L. Pavithra and T. Sree Sharmila, "An improved seed point selection-based unsupervised color clustering for content-based image retrieval application," The Computer Journal, vol. 63, no. 3, pp. 337–350, 2020.
[10] S. Kumar, M. K. Singh, and M. Mishra, "Efficient deep feature based semantic image retrieval," Neural Processing Letters, vol. 55, no. 3, pp. 2225–2248, 2023.
[11] D. Park and Y. Hwang, "Efficient Image Retrieval Using Hierarchical K-Means Clustering," Sensors, vol. 24, no. 8, p. 2401, 2024.
[12] M. Alrahhal and K. Supreethi, "Integrating machine learning algorithms for robust content-based image retrieval," International Journal of Information Technology, vol. 16, no. 8, pp. 5005–5021, 2024.
[13] S. Sikandar, R. Mahum, and A. Alsalman, "A novel hybrid approach for a content-based image retrieval using feature fusion," Applied Sciences, vol. 13, no. 7, p. 4581, 2023.
[14] R. Akash Guna and O. Sikha, "Content-Based Image Retrieval Using Deep Features and Hamming Distance," in Smart Computer Vision: Springer, 2023, pp. 151–179.
[15] H. Qazanfari, H. Hassanpour, and K. Qazanfari, "Content-based image retrieval using HSV color space features," International Journal of Computer and Information Engineering, vol. 13, no. 10, pp. 537–545, 2019.
[16] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-sensitive integrated matching for picture libraries," IEEE Transactions on pattern analysis and machine intelligence, vol. 23, no. 9, pp. 947–963, 2001.
[17] R. Montagna and G. D. Finlayson, "Padua point interpolation and L p-norm minimisation in colour-based image indexing and retrieval," IET Image Processing, vol. 6, no. 2, pp. 139–147, 2012.
[18] H. Farsi and S. Mohamadzadeh, "Colour and texture feature‐based image retrieval by using Hadamard matrix in discrete wavelet transform," IET Image Processing, vol. 7, no. 3, pp. 212–218, 2013.
[19] مقیمیان, م. زاده, محرم, دزفولیان, and میرحسین, "بازیابی تصویر مبتنی بر محتوا با استفاده از همجوشی نتایج چندسطحی," مجله مهندسی برق دانشگاه تبریز, vol. 49, no. 3, pp. 1345–1357, 2019.
[20] ا. شمسی گوشکی, اسما, سریزدی, نظام‌آبادی‌پور, معین, and محمدشهرام, "روشی جدید در بازخورد ربط برای بازیابی تصویر بر اساس محتوا به شیوه چند پرسشی," مجله مهندسی برق دانشگاه تبریز, vol. 40, no. 2, pp. 51–62, 2011.
[21] ط. فاطمه, س. پدرام, and ر. کامبیز, "معرفی یک چارچوب ترکیبی برای بازیابی تصویر مبتنی بر محتوا بر پایه تحلیل بعد فرکتال و ماشین بولتزمن عمیق," 2021.
[22] D. Varga and T. Szirányi, "Fast content-based image retrieval using convolutional neural network and hash function," in 2016 IEEE international conference on systems, man, and cybernetics (SMC), 2016: IEEE, pp. 002636–002640.
[23] سزاوار, فرسی, and محمدزاده, "بازیابی تصویر مبتنی بر محتوا با استفاده از شبکه‌های عصبی کانولوشن عمیق," مجله مهندسی برق دانشگاه تبریز, vol. 48, no. 4, pp. 1595–1603, 2019.
[24] T.-q. Peng and F. Li, "Image retrieval based on deep convolutional neural networks and binary hashing learning," in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2017: IEEE, pp. 1742–1746.
[25] K. Golalipour, E. Akbari, S. S. Hamidi, M. Lee, and R. Enayatifar, "From clustering to clustering ensemble selection: A review," Engineering Applications of Artificial Intelligence, vol. 104, p. 104388, 2021.
[26] M. Ahmed, R. Seraj, and S. M. S. Islam, "The k-means algorithm: A comprehensive survey and performance evaluation," Electronics, vol. 9, no. 8, p. 1295, 2020.
[27] D. Alita, "Multiclass SVM Algorithm for Sarcasm Text in Twitter," JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), vol. 8, no. 1, pp. 118–128, 2021.
[28] A. Belghit, M. Lazri, F. Ouallouche, K. Labadi, and S. Ameur, "Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data," Advances in Space Research, vol. 71, no. 1, pp. 946–963, 2023.
[29] H. Wu, Y.-P. Zhao, and T. Hui-Jun, "A hybrid of fast K-nearest neighbor and improved directed acyclic graph support vector machine for large-scale supersonic inlet flow pattern recognition," Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 236, no. 1, pp. 109–122, 2022.
[30] N. Kayhan and S. Fekri-Ershad, "Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns," Multimedia Tools and Applications, vol. 80, no. 21, pp. 32763–32790, 2021.
[31] M. I. T. Bella and A. Vasuki, "An efficient image retrieval framework using fused information feature," Computers & Electrical Engineering, vol. 75, pp. 46–60, 2019.
[32] U. A. Khan, A. Javed, and R. Ashraf, "An effective hybrid framework for content based image retrieval (CBIR)," Multimedia Tools and Applications, vol. 80, no. 17, pp. 26911–26937, 2021.
[33] K. T. Ahmed, S. Ummesafi, and A. Iqbal, "Content based image retrieval using image features information fusion," Information Fusion, vol. 51, pp. 76–99, 2019.
[34] C. Palai, P. K. Jena, S. R. Pattanaik, T. Panigrahi, and T. K. Mishra, "Content-Based image retrieval using encoder based RGB and texture feature fusion," International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, 2023.
