Content-Based Image Retrieval Using Deep Learning
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The most prevalent and well-used method for obtaining images from huge, unlabelled image datasets is content-based image retrieval. Convolutional Neural Networks are pre-trained deep neural networks which can generate and extract accurate features from image databases. These CNN models have been trained using large databases with thousands of classes that include a huge number of images, making it simple to use their information. Based on characteristics retrieved using the pre-trained CNN models, we created CBIR systems in the work. These pre-trained CNN models VGG16, and MobileNet have been employed in this instance to extract sets of features that are afterward saved independently and used for image retrieval.
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Research works printed but not linked: 1
- [1][1] J C Karur, Asma Hebbal, Jagadeesh Pujari, " Content-Based Image Retrieval Using Deep Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.122-128, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT228418 Peer reviewed and Refereed International Scientific Research Journal Online ISSN : 2456-3307 Submit your paper to the following e-mails:editor@ijsrcseit.com,ijsrcseit@gmail.com Website © Technoscience Academy. Published articles are licensed under CC BY 4.0 — article content is not “all rights reserved.” | Platform and Workflow by OJS/PKP
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“The image similarity calculation using a MobileNetV2 convolutional neural network does not use the hash algorithm, it uses cosine similarity as a measure of the degree of similarity between image features [38].”
- Authors
- Karur, D. J. C., Hebbal, A., & Pujari, D. J.
- Affiliations
- Professor, Department of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India
- Mtech Student Department of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India
- HOD Department of Information Science and Engineering, SDM College of Engineering and Technology, Dharwad, Karnataka, India
- Venue
- International Journal of Scientific Research in Computer Science Engineering and Information Technology
- Publisher
- Technoscience Academy
- Published
- 2022-07-10
- Pages
- 122-128
- ISSN
- 2456-3307 (ISSN-L)
- OpenAlex
- W4295232616
- Cited by
- 1
- References
- 25
- Citations by year
- 2025: 1
- FWCI
- 0.08
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Scientific Research in Computer Science Engineering and Information Technology (publishedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based image retrieval, pre-trained convolutional neural networks, VGG16, MobileNet, feature extraction, image database, image retrieval systems
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