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

In computer-based search systems, similarity plays a key role in replicating the human process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The for images consists of establishing correspondence between available and those sought by user, measuring images. fact, per content is generally based on visual characteristics distance function used to evaluate depends not only criteria but also representation image. This main idea content-based retrieval system. this article, we first constructed type-2 beta fuzzy membership descriptor vectors help manage inaccuracy uncertainty extracted from feature Subsequently, retrieved are ranked according novel measure, noted nearness measure (IT2FNM). By analogy logic, motivated near sets theory, advanced new (FSM) IT2FNM. Then, propose three IT2FSMs provide mathematical justification demonstrate that proposed FSMs satisfy proximity properties (i.e., reflexivity, transitivity, symmetry, overlapping). experimental results generated using databases show consistent significant results.