Color Image Segmentation using Type-2 Fuzzy Sets

Abstract

Abstract--Domain knowledge of real life problems are often uncertain, imprecise and inexact, therefore create difficulty in decision making while solving by conventional approaches.Among various methods of handling uncertainties, fuzzy logic has been most intensively studied almost over four decades. Fuzzy logic (FL) explores human reasoning power using linguistic terms, which are modeled as type-1 fuzzy sets and represented by membership functions (MF). However, the MF of type-1 fuzzy set is crisp and cannot tackle all kind of uncertainties. Introducing type-2 fuzzy sets, an extension of type-1 simplifies the problem where the MF is itself fuzzy with three dimension representations. Moreover, a type-2 fuzzy set maps elements of a crisp domain to type-1 fuzzy numbers bounded in the range [0, 1]. Research on type-2 fuzzy sets is still in its infancy and very recently has been applied on emerging areas that need development of more efficient and robust systems. The aim of the review paper is to describe type-2 fuzzy systems for managing uncertainties, presenting the frontier research areas where type-2 fuzzy logic has been applied and proposes algorithm on application of type-2 fuzzy sets in color image segmentation.

Industrial applications of type-2 fuzzy sets and systems: A concise review

“…They presented a demonstration of how the type-2 fuzzy logic model works and outlined directions for [70] Color image segmentation Since handling uncertainty in image analysis is relatively difficult Zeng and Liu (2008) [71] Handwritten Chinese character recognition To handle both fuzziness and randomness in the structural pattern representation Saremi and Montazer (2008) [72] website structures selection to cope with type-2 fuzzy environment and data incorporating much more fuzziness in decision making Fisher (2007) [73] Geographical Information Using type-2 fuzzy sets seems more appropriate Bustince et al (2007) [ [88] Adaptive information retrieval To enforce the dynamic link between relevance and uncertainty in keyword based description systems further research into the digital enterprise technology for time to market.…”

“…Since handling uncertainty in image analysis is relatively difficult, Maity and Sil [70] proposed an algorithm based on type-2 fuzzy sets for color image segmentation. They preferred using type-2 fuzzy sets, which was not been applied before.…”

“…Type-2 MFs are threedimensional when we consider the weights (w xi ) as possibilities associated with value of x in the membership domain. The new third dimension provides new design degrees of freedom for handling secondary uncertainties [1,12].…”

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

“…Moreover, Type-2 MFs are three-dimensional which consider possibilities by using the weights in the membership domain. The novel third dimension gives new design degrees of freedom for coping with secondary uncertainties [38,42].…”

Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

“…Now T2FS prove to model various uncertainties but it increases the computational complexity, because of its additional dimension of secondary grades for each primary membership. Example applications are Type-2 Fuzzy Clustering [1], Gaussian Noise Filter [15], Classification of coded video streams [24], Medical applications [23] and Color image segmentation [8].…”