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Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

Yosr, Ghozzi and Baklouti, Nesrine and Hagras, Hani and Ben ayed, Mounir and Alimi, Adel M (2022) 'Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval.' IEEE Transactions on Fuzzy Systems, 30 (3). pp. 805-817. ISSN 1063-6706

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In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results.

Item Type: Article
Uncontrolled Keywords: Interval-Type-2 Fuzzy Sets, Near Sets, Function Beta, Fuzzy similarity measure, CBIR
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Jan 2021 15:44
Last Modified: 25 Nov 2022 15:07

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