Ghozzi, Yosr and Hamdani, Tarek M and Hagras, Hani and Ouahada, Khmaies and Chabchoub, Habib and Alimi, Adel M (2024) A deep learning based interval type-2 fuzzy approach for image retrieval systems. Neurocomputing, 603. p. 128251. DOI https://doi.org/10.1016/j.neucom.2024.128251
Ghozzi, Yosr and Hamdani, Tarek M and Hagras, Hani and Ouahada, Khmaies and Chabchoub, Habib and Alimi, Adel M (2024) A deep learning based interval type-2 fuzzy approach for image retrieval systems. Neurocomputing, 603. p. 128251. DOI https://doi.org/10.1016/j.neucom.2024.128251
Ghozzi, Yosr and Hamdani, Tarek M and Hagras, Hani and Ouahada, Khmaies and Chabchoub, Habib and Alimi, Adel M (2024) A deep learning based interval type-2 fuzzy approach for image retrieval systems. Neurocomputing, 603. p. 128251. DOI https://doi.org/10.1016/j.neucom.2024.128251
Abstract
Deep learning, that one of its key benefits is automated feature extraction, has become a principal solution for computer vision. This paper presents a Deep Type-2 Beta Fuzzy (DT2F) approach for Content-Based Image Retrieval (CBIR) systems. Firstly, the suggested architecture uses InceptionResNetv2 a pre-trained deep learning model on Image-Net data as a feature extractor. Secondly, the obtained feature space is fuzzified to handle the uncertainties associated with the extracted values of deep features. Thirdly, the reduction dimensionality step is efficiently applied using a Multi-Variational Auto-Encoder (MVAE) to reduce computational complexity and achieve better performance. Ultimately, we retrieve images using the nearest neighbors rule based on type-2 fuzzy similarity to having higher proximity sensitivity. Extensive experimentations were accomplished on various image-retrieving datasets of different scales the proposed system achieved an average precision of 97.15% exceeding other state-of-the-art methods over many systems on Corel datasets, which can open the door for several hybridization breakthroughs in the area of image retrieval.
Item Type: | Article |
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Uncontrolled Keywords: | Deep learning; Variational auto-encoder; Reduction dimensionality; Function beta; Image retrieval |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Aug 2024 07:37 |
Last Modified: | 04 Sep 2024 16:06 |
URI: | http://repository.essex.ac.uk/id/eprint/38974 |
Available files
Filename: DeepFuzzyIR (7).pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Embargo Date: 31 July 2025