A beta-fuzzy-near-sets approach to research for visually similar content images

In the automated search system, similarity is a key concept for solving the human task. The human process is a natural categorization, which underlies many natural abilities such as image recovery, language comprehension, decision making or pattern recognition. In this paper, the focus is on the use of similarities in image retrieval search using near sets of similarity approaches. The results showed that a general framework for Near set is compatible with these foundations, and that similarity measurements can be involved in all steps of the image research process. We therefore focus on the fuzzy logic which provides interesting tools for data mining mainly because of its ability to represent imperfect information. We then introduce a new category of a fuzzy set : the Beta function. We finally illustrate our work with examples of similarities used in the real world of image retrieval problems.