ABSTRACT
Most of the problems in medical science, engineering and environmental science do not always involve crisp data. And traditional methods may not be used successfully when uncertainties are involved. Many new approaches and theories are introduced since the introduction of fuzzy set and have showed successful applications in various fields. These are similarity, distance or entropy measures that are the vital in image processing and are used in many image processing applications such as image retrieval, registration and segmentation. Similarity measure indicates that the degree of similarity between two fuzzy sets and entropy denotes the fuzziness in a fuzzy set. But fuzzy sets consider only one uncertainty which is the degree of belongingness or membership degree. But in reality, it may not always be certain that the non-membership degree in a fuzzy set is just equal to 1 minus the degree of membership. Many researchers extended fuzzy measures [1,7,10,13,24] using intuitionistic fuzzy set (IFS) which are characterized by membership and non-membership functions. Intuitionistic fuzzy-based models may be adequate in many situations when we face human opinions such as ‘yes’ or ‘no’ or ‘does not apply’, more specifically in voting where people can vote for or against or does not vote. Using these sets, new approaches such as fuzzy distance/similarity/entropy measures are extended. Such a generalization of fuzzy set gives us additional information to represent imperfect knowledge that will help in describing many real-time problems accurately. Different types of similarity/distance/ entropy measures using IFS are discussed later.
