Intuitionistic fuzzy negations and their use in image classification

Abstract

In this paper, the problem of classification of images is discussed. Our specific problem is that we need to classify tire images into selected classes. The classes are characterized by some patterns. In the first step images are represented as the vectors. Then the membership and non-membership value to each coordinate of the vector is calculated and the theory of intuitionistic fuzzy sets is used. In [7] the classification of images was performed with respect to the valued of so called Sim function, which was defined as a ratio of distance between pattern data and image data and distance between pattern data and the complement of image data. The complement of image data was obtained by using specific intuitionistic fuzzy negation. In [2] a list of 53 intuitionistic fuzzy negations was presented. We have decided to use some of these negations to improve the results of classification.

Some notes on intuitionistic fuzzy equivalence relations and their use on real data

“…Therefore it is important to process the images in such way that the best possible position of tire tread sample will be obtained. On the behalf of this idea there was created number of methods (see [11][12][13]19]) of classifying images into the chosen number of classes. In this part of the paper we use the results of classification of the images on the basis of intuitionistic fuzzy sets and we apply the conditions of intuitionistic fuzzy equivalence relations to obtain new results.…”