Enhanced Convolutional Neural Network for Efficient Content-Based Image Retrieval
DOI:
https://doi.org/10.22399/ijcesen.937Keywords:
Artificial Intelligence, Deep Learning, Convolutional Neural Network, Content-Based Image RetrievalAbstract
The use of picture objects in various real-world applications has increased dramatically with the rise of cloud-based ecosystems for managing, analyzing, and storing multimedia material. CBIR is a method for obtaining photos from the cloud and other storage infrastructures. It involves using an image input to look for images that match the database. Because of its methodology, this phenomenon is deemed preferable to text-based search. However, conventional CBIR techniques rely on similarity and feature comparison metrics. As AI grows, learning-based approaches are also shown to be beneficial for matching semantic material. Therefore, we presented a deep learning architecture to achieve an effective learning-based CBIR system in this research. To improve the matching experience in image retrieval, we suggested a modified CNN model for feature extraction from images. We proposed the Intelligent Content-Based Image Retrieval (ICBIR) algorithm. For our tests, we used the ImageNet micro dataset. The suggested modified CNN model-based CBIR system performs better than current techniques in picture retrieval that as closely resembles user intent as feasible, according to experimental data.
References
Wiggers, K. L., Britto, A. S., Heutte, L., Koerich, A. L., & Oliveira, L. S. (2019). Image Retrieval and Pattern Spotting using Siamese Neural Network. International Joint Conference on Neural Networks (IJCNN). 1-8. https://doi.org/10.1109/ijcnn.2019.8852197
Kumar, S., Pal, A.K., Varish, N., Nurhidayat, I., Eldin, S.M., & Sahoo, S.K. (2023). A hierarchical approach-based CBIR scheme using shape, texture, and color for accelerating the retrieval process. Journal of King Saud University-Computer and Information Sciences. 35(7);1-20. https://doi.org/10.1016/j.jksuci.2023.101609
Hasoon, J. N., & Hassan, R. (2019). Face Image Retrieval Based on Fireworks Algorithm. AL-Noor International Conference for Science and Technology (NICST). 94-99. https://doi.org/10.1109/nicst49484.2019.9043786
Ahmed, K. T., Aslam, S., Afzal, H., Iqbal, S., Mehmood, A., & Choi, G. S. (2021). Symmetric Image Contents Analysis and Retrieval using Decimation, Pattern Analysis, Orientation, and Features Fusion. IEEE Access. 9;57215-57242. https://doi.org/10.1109/access.2021.3071581
Kruthika, K. R., Rajeswari, & Maheshappa, H. D. (2018). CBIR System Using Capsule Networks and 3D CNN for Alzheimer's disease Diagnosis. Informatics in Medicine Unlocked. 14;5-68. https://doi.org/10.1016/j.imu.2018.12.001
Hatibaruah, R., Nath, V. K., & Hazarika, D. (2020). Biomedical CT Image Retrieval Using 3D Local Oriented Zigzag Fused Pattern. National Conference on Communications (NCC). 1-6. https://doi.org/10.1109/ncc48643.2020.9056038
Celebi, M. E., Codella, N., & Halpern, A. (2019). Dermoscopy Image Analysis: Overview and Future Directions. IEEE Journal of Biomedical and Health Informatics. 23(2);474-478. https://doi.org/10.1109/jbhi.2019.2895803
Gupta, S., Thakur, K., & Kumar, M. (2020). 2D-human face recognition using SIFT and SURF descriptors of face's feature regions. The Visual Computer. 1-10. https://doi.org/10.1007/s00371-020-01814-8
Allegretti, S., Bolelli, F., Pollastri, F., Longhitano, S., Pellacani, G., & Grana, C. (2021). Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval. International Conference on Pattern Recognition (ICPR). 1-8. https://doi.org/10.1109/icpr48806.2021.941241
Yan, C., Gong, B., Wei, Y., & Gao, Y. (2020). Deep Multi-View Enhancement Hashing for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1-8. https://doi.org/10.1109/tpami.2020.2975798
Schall, K., Barthel, K. U., Hezel, N., & Jung, K. (2019). Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval. IEEE International Workshop on Multimedia Signal Processing (MMSP). 1-6. https://doi.org/10.1109/mmsp.2019.8901787
Majhi, M., & Pal, A. K. (2020). An image retrieval scheme based on block level hybrid dct-svd fused features. Multimedia Tools and Applications. 1-42. https://doi.org/10.1007/s11042-020-10005-5
Dhingra, S., & Bansal, P. (2020). Experimental analogy of different texture feature extraction techniques in image retrieval systems. Multimedia Tools and Applications. 1-16. https://doi.org/10.1007/s11042-020-09317-3
Ahmed, K. T., Jaffar, S., Hussain, M. G., Fareed, S., Mehmood, A., & Choi, G. S. (2021). Maximum Response Deep Learning Using Markov, Retinal & Primitive Patch Binding With GoogLeNet & VGG-19 for Large Image Retrieval. IEEE Access. 9;41934-41957. https://doi.org/10.1109/access.2021.3063545
Freire, D.L., Ponce de Leon Ferreira de Carvalho, A.C., Carneiro Feltran, L., Ayumi Nagamatsu, L., Ramos da Silva, K.C., Firmino, C., Ferreira, J.E., Losco Takecian, P., Carlotti, D., Cavalcanti Lima, F.A., & Mendes Portela, R. (2022). Content-Based Lawsuits Document Image Retrieval. EPIA Conference on Artificial Intelligence. 29-40. https://doi.org/10.1007/978-3-031-16474-3_3
Yang, F., Ismail, N.A., Pang, Y.Y., Kebande, V.R., Ai-Dhaqm, A., & Koh, T.W. (2024). A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions. IEEE Access. 12;14847-14869. https://doi.org/10.1109/ACCESS.2024.3298216
Danapur, N., Dizaj, S. A. A., & Rostami, V. (2020). An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learning. Multimedia Tools and Applications. 1-24. https://doi.org/10.1007/s11042-020-09109-9
Das, P., & Neelima, A. (2020). A Robust Feature Descriptor for Biomedical Image Retrieval. IRBM. 1-13. https://doi.org/10.1016/j.irbm.2020.06.007
Sudha, S. K., & Aji, S. (2019). A Review on Recent Advances in Remote Sensing Image Retrieval Techniques. Journal of the Indian Society of Remote Sensing. 47(12);2129-2139. https://doi.org/10.1007/s12524-019-01049-8
Zhan, Z., Zhou, G., & Yang, X. (2020). A Method of Hierarchical Image Retrieval for Real-Time Photogrammetry Based on Multiple Features. IEEE Access. 8;21524-21533. https://doi.org/10.1109/access.2020.2969287
Dubey, S. R. (2021). A Decade Survey of Content Based Image Retrieval using Deep Learning. IEEE Transactions on Circuits and Systems for Video Technology. 1-17. https://doi.org/10.1109/tcsvt.2021.3080920
Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Zakir, A., Ahmad, S., & Lu, J. (2019). Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning. IEEE Access. 1-13. https://doi.org/10.1109/access.2019.2892455
Li, X., Yang, J., & Ma, J. (2021). Recent developments of content-based image retrieval (CBIR). Neurocomputing. 452;675-689. https://doi.org/10.1016/j.neucom.2020.07.139
Shamna, P., Govindan, V. K., & Abdul Nazeer, K. A. (2019). Content based medical image retrieval using topic and location model. Journal of Biomedical Informatics. 91;1-16. https://doi.org/10.1016/j.jbi.2019.103112
Passalis, N., Iosifidis, A., Gabbouj, M., & Tefas, A. (2019). Variance-preserving Deep Metric Learning for Content-based Image Retrieval. Pattern Recognition Letters. 1-12. https://doi.org/10.1016/j.patrec.2019.11.041
Tuyet, V. T. H., Binh, N. T., Quoc, N. K., & Khare, A. (2021). Content Based Medical Image Retrieval Based on Salient Regions Combined with Deep Learning. Mobile Networks and Applications. 26(3);1300-1310. https://doi.org/10.1007/s11036-021-01762-0
Punithavathi, R., Ramalingam, A., Kurangi, C., Reddy, A. S. K., & Uthayakumar, J. (2021). Secure content based image retrieval system using deep learning with multi share creation scheme in cloud environment. Multimedia Tools and Applications. 1-22. https://doi.org/10.1007/s11042-021-10998-7
Vieira, G.S., Fonseca, A.U., & Soares, F. (2023). CBIR-ANR: A content-based image retrieval with accuracy noise reduction. Software Impacts. 15;1-7. https://doi.org/10.1016/j.simpa.2023.100486
Arai, H., Onga, Y., Ikuta, K., Chayama, Y., Iyatomi, H., & Oishi, K. (2021). Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI. IEEE Access. 9;165326-165340. https://doi.org/10.1109/ACCESS.2021.3129105
Arora, N., Kakde, A., & Sharma, S.C. (2023). An optimal approach for content-based image retrieval using deep learning on COVID-19 and pneumonia X-ray Images. International Journal of System Assurance Engineering and Management. 14(Suppl 1);246-255. https://doi.org/10.1007/s13198-022-01846-4
Agrawal, S., Chowdhary, A., Agarwala, S., Mayya, V., & Kamath, S.S. (2022). Content-based medical image retrieval system for lung diseases using deep CNNs. International Journal of Information Technology. 14(7);3619-3627. https://doi.org/10.1007/s41870-022-01007-7
Shamna, P., Govindan, V.K., & Abdul Nazeer, K.A. (2022). Content-based medical image retrieval by spatial matching of visual words. Journal of King Saud University - Computer and Information Sciences. 34(2);58-71. https://doi.org/10.1016/j.jksuci.2018.10.002
Wang, Y., Wang, F., Liu, F., & Wang, X. (2022). Securing content-based image retrieval on the cloud using generative models. Multimedia Tools and Applications. 81;31219-31243. https://doi.org/10.1007/s11042-022-12880-6
Kumar, G.V.R.M., & Madhavi, D. (2023). Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-based Image Retrieval. IEEE Access. 11;77452-77463. https://doi.org/10.1109/ACCESS.2023.3298216
Öztürk, Ş., Çelik, E., & Çukur, T. (2023). Content-based medical image retrieval with opponent class adaptive margin loss. Information Sciences. 637;1-10. https://doi.org/10.1016/j.ins.2023.118938
Röhrich, S., Heidinger, B.H., Prayer, F., Weber, M., Krenn, M., Zhang, R., Sufana, J., Scheithe, J., Kanbur, I., Korajac, A., Pötsch, N., Raudner, M., Al-Mukhtar, A., Fueger, B.J., Milos, R.I., Scharitzer, M., Langs, G., & Prosch, H. (2023). Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal. European Radiology. 33;360-367. https://doi.org/10.1007/s00330-022-08973-3
Xing, Y., Meyer, B.J., Harandi, M., Drummond, T., & Ge, Z. (2023). Multimorbidity content-based medical image retrieval and disease recognition using multi-label proxy metric learning. IEEE Access. 11;50165-50179. https://doi.org/10.1109/ACCESS.2023.3278376
Gautam, G., & Khanna, A. (2024). Content Based Image Retrieval System Using CNN based Deep Learning Models. Procedia Computer Science. 235;3131-3141. https://doi.org/10.1016/j.procs.2024.04.296
Li, M., Jung, Y., Fulham, M., & Kim, J. (2024). Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study. Virtual Reality & Intelligent Hardware. 6(1);71-81. https://doi.org/10.1016/j.vrih.2023.08.005
Issaoui, I., Alohali, M.A., Mtouaa, W., Alotaibi, F.A., Mahmud, A., & Assiri, M. (2024). Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector. IEEE Access. 12;29768-29777. https://doi.org/10.1109/ACCESS.2024.3367430
ZHANG, J. (2025). Artificial intelligence contributes to the creative transformation and innovative development of traditional Chinese culture. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.860
Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18
M.K. Sarjas, & G. Velmurugan. (2025). Bibliometric Insight into Artificial Intelligence Application in Investment. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.864
Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research 2(1). https://doi.org/10.22399/ijasrar.19
G. Prabaharan, S. Vidhya, T. Chithrakumar, K. Sika, & M.Balakrishnan. (2025). AI-Driven Computational Frameworks: Advancing Edge Intelligence and Smart Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1165
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
