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Deep Representation-Based Fuzzy Graph Model for Content-Based Image Retrieval.
- Source :
- International Journal of Fuzzy Systems; Sep2024, Vol. 26 Issue 6, p2011-2022, 12p
- Publication Year :
- 2024
-
Abstract
- Image retrieval involves searching for images relevant to a user-provided query image. In this paper, we aim to develop a graph-based model with deep representations for Content-Based Image Retrieval (CBIR). Inspired by recent advancements in deep learning, we initially employ a fine-tuned Convolutional Neural Network (CNN) to capture deep semantic features for a specific target image database. Utilizing these learned features, we then introduce an graph-based ranking method for online retrieval. This model's constructed graph is designed to characterize the geometrical structure of the data manifold, facilitating an efficient ranking process. Finally, based on user-provided feedback regarding relevant and irrelevant images, we update the retrieval system in both the deep learning framework and the graph-based ranking model in an offline manner. Extensive simulations confirm the efficiency and effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15622479
- Volume :
- 26
- Issue :
- 6
- Database :
- Supplemental Index
- Journal :
- International Journal of Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 179235360
- Full Text :
- https://doi.org/10.1007/s40815-024-01682-7