• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 25, Pages: 2610-2621

Original Article

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Received Date:13 March 2024, Accepted Date:03 June 2024, Published Date:19 June 2024


Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings.

Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database


  1. Li X, Yang J, Ma J. Recent developments of content-based image retrieval (CBIR) Neurocomputing. 2021;452:675–689. Available from: https://doi.org/10.1016/j.neucom.2020.07.139
  2. Alsmadi MK. Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features. Arabian Journal for Science and Engineering. 2020;45(4):3317–3330. Available from: https://doi.org/10.1007/s13369-020-04384-y
  3. Madduri A. Content based Image Retrieval System using Local Feature Extraction Techniques. International Journal of Computer Applications. 2021;183(20):16–20. Available from: https://www.ijcaonline.org/archives/volume183/number20/32039-2021921549/
  4. Singh S, Batra S. An efficient bi-layer content based image retrieval system. Multimedia Tools and Applications. 2020;79:17731–17759. Available from: https://doi.org/10.1007/s11042-019-08401-7
  5. Keisham N, Neelima A. Efficient content-based image retrieval using deep search and rescue algorithm. Soft Computing. 2022;26(4):1597–1616. Available from: https://doi.org/10.1007/s00500-021-06660-x
  6. Kashif M, Raja G, Shaukat F. An efficient content-based image retrieval system for the diagnosis of lung diseases. Journal of digital imaging. 2020;33(4):971–987. Available from: https://doi.org/10.1007/s10278-020-00338-w
  7. Passalis N, Iosifidis A, Gabbouj M, Tefas A. Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters. 2020;131:8–14. Available from: https://doi.org/10.1016/j.patrec.2019.11.041
  8. Rajasenbagam T, Jeyanthi S, Pandian JA. Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques. Journal of Ambient Intelligence and Humanized Computing. 2021;p. 1–8. Available from: https://doi.org/10.1007/s12652-021-03075-2
  9. Bibi R, Mehmood Z, Yousaf RM, Saba T, Sardaraz M, Rehman A. Query-by-visual-search: multimodal framework for content-based image retrieval. Journal of Ambient Intelligence and Humanized Computing. 2020;11:5629–5648. Available from: https://doi.org/10.1007/s12652-020-01923-1
  10. Karthik K, Kamath SS. A deep neural network model for content-based medical image retrieval with multi-view classification. A deep neural network model for content-based medical image retrieval with multi-view classification. The Visual Computer. 2021;37:1837–1850. Available from: https://doi.org/10.1007/s00371-020-01941-2
  11. Kumar RB, Marikkannu P. An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application. Multimedia Tools and Applications. 2020;79:22277–22292. Available from: https://doi.org/10.1007/s11042-020-08953-z
  12. Hassan A, Liu F, Wang F, Wang Y. Secure content based image retrieval for mobile users with deep neural networks in the cloud. Journal of Systems Architecture. 2021;116. Available from: https://doi.org/10.1016/j.sysarc.2021.102043
  13. Ranjith E, Parthiban L, Latchoumi TP, Kumar SA, Perera DG, Ramaswamy S. An Effective Content Based Image Retrieval System Using Deep Learning Based Inception Model. Wireless Personal Communications . 2023;133:811–829. Available from: https://doi.org/10.1007/s11277-023-10792-8
  14. Hameed IM, Abdulhussain SH, Mahmmod BM, Pham DT. Content-based image retrieval: A review of recent trends. Cogent Engineering . 2021;8(1):1–37. Available from: https://doi.org/10.1080/23311916.2021.1927469
  15. Jordan T, Elgazzar H. Content-Based Image Retrieval Using Deep Learning. In: Advances in Data Science and Information Engineering , Transactions on Computational Science and Computational Intelligence. (pp. 771-785) Springer, Cham. 2021.
  16. Dubey SR. A decade survey of content based image retrieval using deep learning. IEEE Transactions on Circuits and Systems for Video Technology. 2021;32(5):2687–2704. Available from: https://doi.org/10.1109/TCSVT.2021.3080920
  17. Zhou X, Han X, Li H, Wang J, Liang X. Cross-domain image retrieval: methods and applications. International Journal of Multimedia Information Retrieval. 2022;11(3):199–218. Available from: https://doi.org/10.1007/s13735-022-00244-7
  18. Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, et al. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology. 2021;302(1):187–197. Available from: https://dx.doi.org/10.1148/radiol.2021204164
  19. Kelishadrokhi MK, Ghattaei M, Fekri-Ershad S. Innovative local texture descriptor in joint of human-based color features for content-based image retrieval. Signal, Image and Video Processing. 2023;17(8):4009–4017. Available from: https://dx.doi.org/10.1007/s11760-023-02631-x
  20. Hu Z, Bors AG. Co-attention enabled content-based image retrieval. Neural Networks. 2023;164:245–263. Available from: https://dx.doi.org/10.1016/j.neunet.2023.04.009
  21. Srivastava D, Singh SS, Rajitha B, Verma M, Kaur M, Lee HN. Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters. IEEE Access. 2023;11:95410–95431. Available from: https://dx.doi.org/10.1109/access.2023.3308911
  22. Taheri F, Rahbar K, Salimi P. Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine. Multimedia Tools and Applications. 2023;82(24):37959–37982. Available from: https://dx.doi.org/10.1007/s11042-022-13670-w
  23. Darapureddy N, Karatapu N, Battula TK. Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization. International Journal of Imaging Systems and Technology. 2021;31(2):828–853. Available from: https://doi.org/10.1002/ima.22475
  24. Sivakumar M, Kumar NM, Karthikeyan N. An Efficient Deep Learning-based Content-based Image Retrieval Framework. Computer Systems Science & Engineering. 2022;43(2):683–700. Available from: https://doi.org/10.32604/csse.2022.021459


© 2024 Anish & Thiyagarajan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.