Interval type-2 Beta Fuzzy Near set based approach to content based image retrieval

TL;DR

This paper introduces a novel approach combining interval type-2 Beta fuzzy sets with near set theory to improve content-based image retrieval by better modeling image features and similarity measures.

Contribution

It proposes a new beta type-2 fuzzy set framework integrated with near set theory for enhanced image similarity assessment in retrieval systems.

Findings

Effective in real-world image recovery scenarios

Improves accuracy of image similarity measurement

Demonstrates advantages over traditional methods

Abstract

In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world.