Designing an Efficient Multi-Domain Feature Analysis Engine with Incremental Learning for Continuous Feedback-Based Image Retrieval.
Keywords:
Feature Extraction, Incremental Learning, Continuous Feedback, CBIR, Multidomain, FeaturesAbstract
Due to its applications in a variety of fields, including healthcare, surveillance, and e-commerce, content-based image retrieval (CBIR) has recently attracted a lot of attention. By introducing an effective multidomain feature analysis engine that includes incremental learning and continuous feedback operations, this paper introduces a novel method for CBIR. The suggested framework combines a variety of feature extraction methods, such as Fourier, Entropy, Color Map, S, Z, Laplace, GRU, and LSTM, to extract key visual characteristics from images and samples. By effectively maximizing feature variance levels, an Elephant Herding Optimizer (EHO) is used to increase the discriminative power of the chosen features. The automatic selection of the most informative features is made possible by the EHO algorithm, improving retrieval performance levels. The use of a Vector Autoregressive Moving Average (VARMA) model, which successfully captures the temporal dependencies and correlations within the image dataset, further enhances the CBIR process. This model greatly improves retrieval accuracy levels and makes it easier to predict relevant images using the extracted features. Additionally, correlation learning operations are used to incorporate feedback learning, which enables the CBIR system to change and advance over time in response to user feedback. As a result of the system's use of the feedback data, the retrieval process has been improved, yielding better precision, recall, and accuracy values for different datasets & samples. These include ImageNet, MIRFLICKR, and CIFAR-10 as well as various real-world image samples were used in the experimental evaluations. The findings show that the suggested approach outperforms current CBIR models with precision of 0.98, recall of 0.985, and MAP of 0.97 for different use cases. Further, the proposed system achieves superior accuracy scores and displays lower delay values for real-time scenarios. The increasing demand for accurate and effective image retrieval systems across a variety of applications drives the necessity for this work under real-time scenarios. The proposed framework addresses the limitations of existing CBIR models by combining multiple feature extraction techniques, optimizing feature variance, utilizing temporal dependencies, and incorporating continuous feedback learning process. The potential applications of this research are diverse and include retrieval of medical images for diagnosis and treatment, effective image analysis through surveillance systems, and personalized product recommendations through e-commerce platforms. The proposed approach has several advantages over current CBIR models, including improved retrieval accuracy, adaptability to shifting user preferences, and improved performance levels.
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M. A. Aboali, I. Elmaddah and H. E. Abdelmunim, "Neural Textual Features Composition for CBIR," in IEEE Access, vol. 11, pp. 28506-28521, 2023, doi: 10.1109/ACCESS.2023.3259737.
Z. Zhang, W. Lu, X. Feng, J. Cao and G. Xie, "A Discriminative Feature Learning Approach With Distinguishable Distance Metrics for Remote Sensing Image Classification and Retrieval," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 889-901, 2023, doi: 10.1109/JSTARS.2022.3233032.
Z. Xia, L. Jiang, D. Liu, L. Lu and B. Jeon, "BOEW: A Content-Based Image Retrieval Scheme Using Bag-of-Encrypted-Words in Cloud Computing," in IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 202-214, 1 Jan.-Feb. 2022, doi: 10.1109/TSC.2019.2927215.
J. Xiang, N. Zhang, R. Pan and W. Gao, "Fabric Retrieval Based on Multi-Task Learning," in IEEE Transactions on Image Processing, vol. 30, pp. 1570-1582, 2021, doi: 10.1109/TIP.2020.3043877.
J. Pradhan, C. Bhaya, A. K. Pal and A. Dhuriya, "Content-Based Image Retrieval Using DNA Transcription and Translation," in IEEE Transactions on NanoBioscience, vol. 22, no. 1, pp. 128-142, Jan. 2023, doi: 10.1109/TNB.2022.3169701.
H. Arai, Y. Onga, K. Ikuta, Y. Chayama, H. Iyatomi and K. Oishi, "Disease-Oriented Image Embedding With Pseudo-Scanner Standardization for Content-Based Image Retrieval on 3D Brain MRI," in IEEE Access, vol. 9, pp. 165326-165340, 2021, doi: 10.1109/ACCESS.2021.3129105.
G. Sumbul, M. Ravanbakhsh and B. Demir, "Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art no. 5405811, doi: 10.1109/TGRS.2021.3124326.
G. Sumbul and B. Demir, "Plasticity-Stability Preserving Multi-Task Learning for Remote Sensing Image Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5620116, doi: 10.1109/TGRS.2022.3160097.
A. Khan, A. Javed, M. T. Mahmood, M. H. A. Khan and I. H. Lee, "Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval," in IEEE Access, vol. 9, pp. 135608-135629, 2021, doi: 10.1109/ACCESS.2021.3116225.
G. Sumbul, J. Xiang and B. Demir, "Towards Simultaneous Image Compression and Indexing for Scalable Content-Based Retrieval in Remote Sensing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12, 2022, Art no. 5630912, doi: 10.1109/TGRS.2022.3204914.
D. Zhu, H. Zhu, X. Wang, R. Lu and D. Feng, "An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images," in IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 913-926, 1 March-April 2023, doi: 10.1109/TSC.2022.3149847.
Y. Li, J. Ma, Y. Miao, Y. Wang, X. Liu and K. -K. R. Choo, "Similarity Search for Encrypted Images in Secure Cloud Computing," in IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 1142-1155, 1 April-June 2022, doi: 10.1109/TCC.2020.2989923.
Q. Tong et al., "VFIRM: Verifiable Fine-Grained Encrypted Image Retrieval in Multi-Owner Multi-User Settings," in IEEE Transactions on Services Computing, vol. 15, no. 6, pp. 3606-3619, 1 Nov.-Dec. 2022, doi: 10.1109/TSC.2021.3083512.
W. Chen et al., "Deep Learning for Instance Retrieval: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7270-7292, 1 June 2023, doi: 10.1109/TPAMI.2022.3218591.
A. Rossi, M. Hosseinzadeh, M. Bianchini, F. Scarselli and H. Huisman, "Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI," in IEEE Transactions on Medical Imaging, vol. 40, no. 3, pp. 986-995, March 2021, doi: 10.1109/TMI.2020.3043641.
J. Brogan et al., "Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval," in IEEE Transactions on Image Processing, vol. 30, pp. 6892-6905, 2021, doi: 10.1109/TIP.2021.3097175.
S. Roy, E. Sangineto, B. Demir and N. Sebe, "Metric-Learning-Based Deep Hashing Network for Content-Based Retrieval of Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 2, pp. 226-230, Feb. 2021, doi: 10.1109/LGRS.2020.2974629.
H. Zhao, L. Yuan, H. Zhao and Z. Wang, "Global-Aware Ranking Deep Metric Learning for Remote Sensing Image Retrieval," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 8008505, doi: 10.1109/LGRS.2021.3059908.
Y. Ghozzi, N. Baklouti, H. Hagras, M. B. Ayed and A. M. Alimi, "Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval," in IEEE Transactions on Fuzzy Systems, vol. 30, no. 3, pp. 805-817, March 2022, doi: 10.1109/TFUZZ.2021.3049900.
Z. Zinonos, S. Gkelios, A. F. Khalifeh, D. G. Hadjimitsis, Y. S. Boutalis and S. A. Chatzichristofis, "Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology," in IEEE Access, vol. 10, pp. 122-133, 2022, doi: 10.1109/ACCESS.2021.3138050.
P. Staszewski, M. Jaworski, J. Cao and L. Rutkowski, "A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers," in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7913-7920, Dec. 2022, doi: 10.1109/TNNLS.2021.3084633.
S. Gkelios, A. Kastellos, Y. S. Boutalis and S. A. Chatzichristofis, "Universal Image Embedding: Retaining and Expanding Knowledge With Multi-Domain Fine-Tuning," in IEEE Access, vol. 11, pp. 38208-38217, 2023, doi: 10.1109/ACCESS.2023.3267804.
M. Rasoolijaberi et al., "Multi-Magnification Image Search in Digital Pathology," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4611-4622, Sept. 2022, doi: 10.1109/JBHI.2022.3181531.
R. J. Chu, N. Richard, H. Chatoux, C. Fernandez-Maloigne and J. Y. Hardeberg, "Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution," in IEEE Transactions on Image Processing, vol. 30, pp. 4341-4356, 2021, doi: 10.1109/TIP.2021.3071557.
J. Ramalhinho, H. F. J. Tregidgo, K. Gurusamy, D. J. Hawkes, B. Davidson and M. J. Clarkson, "Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver Using Multi-Labelled Content-Based Image Retrieval," in IEEE Transactions on Medical Imaging, vol. 40, no. 3, pp. 1042-1054, March 2021, doi: 10.1109/TMI.2020.3045348.
E. S. Sabry et al., "Image Retrieval Using Convolutional Autoencoder, InfoGAN, and Vision Transformer Unsupervised Models," in IEEE Access, vol. 11, pp. 20445-20477, 2023, doi: 10.1109/ACCESS.2023.3241858.
K. T. Ahmed, S. Jaffar, M. G. Hussain, S. Fareed, A. Mehmood and G. S. Choi, "Maximum Response Deep Learning Using Markov, Retinal & Primitive Patch Binding With GoogLeNet & VGG-19 for Large Image Retrieval," in IEEE Access, vol. 9, pp. 41934-41957, 2021, doi: 10.1109/ACCESS.2021.3063545.
G. Ren, X. Lu and Y. Li, "Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera," in IEEE Access, vol. 9, pp. 56709-56725, 2021, doi: 10.1109/ACCESS.2021.3072055.
M. Huang, L. Dong, W. Dong and G. Shi, "Supervised Contrastive Learning Based on Fusion of Global and Local Features for Remote Sensing Image Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023, Art no. 5208513, doi: 10.1109/TGRS.2023.3275644.
Y. Huang, J. Zhang, L. Pan and Y. Xiang, "Privacy Protection in Interactive Content Based Image Retrieval," in IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 3, pp. 595-607, 1 May-June 2020, doi: 10.1109/TDSC.2018.2793923.
C. Iakovidou, N. Anagnostopoulos, M. Lux, K. Christodoulou, Y. Boutalis and S. A. Chatzichristofis, "Composite Description Based on Salient Contours and Color Information for CBIR Tasks," in IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 3115-3129, June 2019, doi: 10.1109/TIP.2019.2894281.
A. Preethy Byju, B. Demir and L. Bruzzone, "A Progressive Content-Based Image Retrieval in JPEG 2000 Compressed Remote Sensing Archives," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5739-5751, Aug. 2020, doi: 10.1109/TGRS.2020.2969374.
S. Wei, L. Liao, J. Li, Q. Zheng, F. Yang and Y. Zhao, "Saliency Inside: Learning Attentive CNNs for Content-Based Image Retrieval," in IEEE Transactions on Image Processing, vol. 28, no. 9, pp. 4580-4593, Sept. 2019, doi: 10.1109/TIP.2019.2913513.
F. -R. Andrés, R. -M. Eduardo, A. -C. Carlos and L. -S. Fidel, "Image Retrieval System based on a Binary Auto-Encoder and a Convolutional Neural Network," in IEEE Latin America Transactions, vol. 18, no. 11, pp. 1925-1932, November 2020, doi: 10.1109/TLA.2020.9398634.
Z. N. K. Swati et al., "Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning," in IEEE Access, vol. 7, pp. 17809-17822, 2019, doi: 10.1109/ACCESS.2019.2892455.
B. Ferreira, J. Rodrigues, J. Leitão and H. Domingos, "Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories," in IEEE Transactions on Cloud Computing, vol. 7, no. 3, pp. 784-798, 1 July-Sept. 2019, doi: 10.1109/TCC.2017.2669999.
K. Iida and H. Kiya, "Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images," in IEEE Access, vol. 8, pp. 200038-200050, 2020, doi: 10.1109/ACCESS.2020.3035563.
A. Ahmed and S. J. Malebary, "Query Expansion Based on Top-Ranked Images for Content-Based Medical Image Retrieval," in IEEE Access, vol. 8, pp. 194541-194550, 2020, doi: 10.1109/ACCESS.2020.3033504.
H. Erfankhah, M. Yazdi, M. Babaie and H. R. Tizhoosh, "Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images," in IEEE Access, vol. 7, pp. 18354-18367, 2019, doi: 10.1109/ACCESS.2019.2897281.
C. Kang, L. Zhu, X. Qian, J. Han, M. Wang and Y. Y. Tang, "Geometry and Topology Preserving Hashing for SIFT Feature," in IEEE Transactions on Multimedia, vol. 21, no. 6, pp. 1563-1576, June 2019, doi: 10.1109/TMM.2018.2883868.
L. K. Pavithra and T. Sree Sharmila, "An Improved Seed Point Selection-Based Unsupervised Color Clustering for Content-Based Image Retrieval Application," in The Computer Journal, vol. 63, no. 1, pp. 337-350, Jan. 2020, doi: 10.1093/comjnl/bxz017.
A. F. S. Devaraj et al., "An Efficient Framework for Secure Image Archival and Retrieval System Using Multiple Secret Share Creation Scheme," in IEEE Access, vol. 8, pp. 144310-144320, 2020, doi: 10.1109/ACCESS.2020.3014346.
A. F. S. Devaraj et al., "An Efficient Framework for Secure Image Archival and Retrieval System Using Multiple Secret Share Creation Scheme," in IEEE Access, vol. 8, pp. 144310-144320, 2020, doi: 10.1109/ACCESS.2020.3014346.
Naushad Varish, Priyanka Singh, Prannoy Tugiti, Marella Hima Manikanta, Bhavana Yedlapalli, Abhishree Pappusetty, Hiren Kumar Thakkar, Gajendra Sharma, "Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications", Computational Intelligence and Neuroscience, vol. 2023, Article ID 6257573, 18 pages, 2023. https://doi.org/10.1155/2023/6257573
Jun Yan, Hui Liu, "Analysis on the Multimedia Information Retrieval Algorithm Based on Enterprise Correlation Financial Analysis under the Background of Big Data", Advances in Multimedia, vol. 2023, Article ID 5375010, 10 pages, 2023. https://doi.org/10.1155/2023/5375010
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