A Review of the Image Segmentation Methods Using Rough Sets

Abstract

Image segmentation is a major problem in image processing, and at the same time, it is a classical problem. Rough set theory is a set of theories that study the representation, learning, and induction of incomplete data imprecise knowledge, and so on. Rough set theory has good applicability in image segmentation because of its good ability to deal with vague and uncertain problems and its characteristics of fast convergence and avoiding local minima in solving optimization problems. The main content of this paper is to review the existing methods for image segmentation based on rough sets, categorize them, and describe the main ideas, advantages, disadvantages, and conditions of use of each method. Some of the methods for image segmentation based on rough sets utilize only rough sets, but most of them combine rough sets with other theories or methods. Therefore, this paper classifies existing methods for image segmentation based on rough sets according to whether they combine with other theories or methods, and what kind of theories or methods they combine with. This paper also provides an outlook on the development trends of the methods for image segmentation based on rough sets. This paper is written with the aim of making the researchers who are engaged in the methods for image segmentation based on rough sets understand the current status of the research works in this field within a short time.