Aspects of intuitionistic fuzzy sets and possibil ity theory in graphical object classification for CBIR
Tatiana Jaworska · 2009
This article introduces the imprecision approach to high‐level graphical object interpretation. It presents a step towards s oft computing which supports the implementation of a content-based image r etrieval (CBIR) system dealing with graphical object classification. Some crucial aspects of CBIR are presented here to illustrate the problems that we are now struggling with. The main motivation of our researches is to p rovide effective and efficient means for the interpretation of graphical o bject classification. The paper shows how the traditional feature vector meth od extends to match graphical objects, difficult to classify, by applyi ng intuitionistic fuzzy sets and possibility theory. We consider the cases where both classification of objects and their retrieval are modelled with the a id of fuzzy set extensions.