Applications for content-based image retrieval (CBIR) are found in a wide range of industries, including e-commerce, multimedia, and healthcare. CBIR is essential for organising and obtaining visual data from massive databases. Traditional techniques frequently fail to extract high-level, relevant information from images, producing retrieval results that are not ideal. This research introduces a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) approach that integrates deep learning and machine learning techniques to enhance CBIR performance. This dataset comprises image-based data collected from multiple sources. This image data were pre-processed using Adaptive Histogram Equalization (AHE). The features of localized image data were extracted using VGG16. For an efficient CBIR process, a novel CFTAB approach was introduced. It combines both deep and machine learning (ML) methods in the proposed architecture to improve the excellence of image search. To further improve performance, CFTAB incorporates an improved AB algorithm. This algorithm adjusts the threshold levels dynamically within a robust classifier to optimize training outcomes.

Applied Science Research Center Applied Science Private University Amman Jordan

Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India

Department of Computer Science and Engineering Faculty of Engineering and Technology Jain University Bengaluru Karnataka India

Department of Computer Science and Engineering Graphic Era Hill University Dehradun 248002 India

Department of Computer Science and Engineering Vivekananda Global University Jaipur India

Department of CSE Graphic Era Deemed To Be University Dehradun 248002 India

Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB Technical University of Ostrava 70800 Ostrava Czech Republic

University Centre for Research and Development Chandigarh University Gharuan Mohali Punjab India

Zobrazit více v PubMed

Hadid, M. H., Hussein, Q. M., Al-Qaysi, Z. T., Ahmed, M. A. & Salih, M. M. An overview of content-based image retrieval methods and techniques. Iraqi J. Comput. Sci. Math.4(3), 66–78 (2023).

Sampathila, N., Pavithra, & Martis, R. J. Computational approach for content-based image retrieval of K-similar images from brain MR image database. Expert Syst.39(7), e12652 (2022).

Zenggang, X. et al. Research on image retrieval algorithms based on a combination of color and shape features. J. Signal Process. Syst.93, 139–146 (2021).

Singh, P., Hrisheekesha, P. N. & Singh, V. K. CBIR-CNN: content-based image retrieval on celebrity data using deep convolution neural network. Recent Adv. Comput. Sci. Commun. (Formerly: Recent Patents on Computer Science)14(1), 257–272 (2021).

Kruthika, K. R., Maheshappa, H. D. & Initiative, A. D. N. CBIR system using Capsule Networks and 3D CNN for Alzheimer’s disease diagnosis. Inform. Med. Unlocked14, 59–68 (2019).

Bianconi, F., Fernández, A., Smeraldi, F. & Pascoletti, G. Colour and texture descriptors for visual recognition: A historical overview. J. Imaging7(11), 245 (2021). PubMed PMC

Bhunia, A. K., Bhattacharyya, A., Banerjee, P., Roy, P. P. & Murala, S. A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Anal. Appl.23, 703–723 (2020).

Arora, N. & Ashok, A., Searching Images with Images: Efficient Image Retrieval Technique using Colour and Texture Features.

Ahmad, K., Sahu, M., Shrivastava, M., Rizvi, M. A. & Jain, V. An efficient image retrieval tool: query-based image management system. Int. J. Inf. Technol.12, 103–111 (2020).

Artemi, M. A. A. A theory-based content-based image retrieval approach for capturing user preferences during query formulation (2021).

Khan, U. A., Javed, A. & Ashraf, R. An effective hybrid framework for content based image retrieval (CBIR). Multimed. Tools Appl.80, 26911–26937 (2021).

Chen, F., Zhao, W. & Zhou, X. Cross-image siamese graph convolutional network for fine-grained image retrieval in diabetic retinopathy. Biomed. Signal Process. Control92, 106045 (2024).

Keisham, N. & Neelima, A. Efficient content-based image retrieval using deep search and rescue algorithm. Soft Comput.26(4), 1597–1616 (2022).

Sundararajan, S. K., Sankaragomathi, B. & Priya, D. S. Deep belief CNN feature representation based content based image retrieval for medical images. J. Med. Syst.43, 1–9 (2019). PubMed

Rastegar, H. & Giveki, D. Designing a new deep convolutional neural network for content-based image retrieval with relevance feedback. Comput. Electr. Eng.106, 108593 (2023).

Bibi, R. et al. Query-by-visual-search: multimodal framework for content-based image retrieval. J. Ambient Intell. Human. Comput.11, 5629–5648 (2020).

Li, X., Yang, J. & Ma, J. Recent developments of content-based image retrieval (CBIR). Neurocomputing452, 675–689 (2021).

Zheng, Q., Tian, X., Yang, M. & Wang, H. Differential learning: A powerful tool for interactive content-based image retrieval. Eng. Lett.27(1) (2019).

Ghahremani, M., Ghadiri, H. & Hamghalam, M. Local features integration for content-based image retrieval based on color, texture, and shape. Multimed. Tools Appl.80(18), 28245–28263 (2021).

Haji, M. S., Alkawaz, M. H., Rehman, A. & Saba, T. Content-based image retrieval: A deep look at features prospectus. Int. J. Comput. Vis. Robot.9(1), 14–38 (2019).

Wang, Y., Chen, L., Wu, G., Yu, K. & Lu, T. Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing. Comput. Security128, 103163 (2023).

Kashif, M., Raja, G. & Shaukat, F. An efficient content-based image retrieval system for the diagnosis of lung diseases. J. Digit. Imaging33(4), 971–987 (2020). PubMed PMC

Ahmed, A. Implementing relevance feedback for content-based medical image retrieval. IEEE Access8, 79969–79976 (2020).

Zhang, K. et al. Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images. Comput. Biol. Med.140, 105096 (2022). PubMed

Unar, S., Wang, X., Wang, C. & Wang, Y. A decisive content based image retrieval approach for feature fusion in visual and textual images. Knowl.-Based Syst.179, 8–20 (2019).

Zhang, X., Bai, C. & Kpalma, K. OMCBIR: Offline mobile content-based image retrieval with lightweight CNN optimization. Displays76, 102355 (2023).

Wei, S. et al. Saliency inside: Learning attentive CNNs for content-based image retrieval. IEEE Trans. Image Process.28(9), 4580–4593 (2019). PubMed

Yang, Y. et al. Image retrieval via learning content-based deep quality model towards big data. Future Gener. Comput. Syst.112, 243–249 (2020).

Yenigalla, S. C., Rao, K. S. & Ngangbam, P. S. Implementation of content-based image retrieval using artificial neural networks. Eng. Proc.34(1), 25 (2023).

Salih, F. A. A. & Abdulla, A. A. An efficient two-layer based technique for content-based image retrieval. UHD J. Sci. Technol.5(1), 28–40 (2021).

Salih, S. F. & Abdulla, A. A. An improved content based image retrieval technique by exploiting bi-layer concept. UHD J. Sci. Technol.5(1), 1–12 (2021).

Sivakumar, M., Kumar, N. M. & Karthikeyan, N. An efficient deep learning-based content-based image retrieval framework. Comput. Syst. Sci. Eng.43(2), 693–700 (2022).

Mohammed, M. A., Hussain, M. A., Oraibi, Z. A., Abduljabbar, Z. A. & Nyangaresi, V. O. Secure content based image retrieval system using deep learning. J. Basrah Res. (Sci.)49(2), 94–111 (2023).