Standard and Fuzzy Rough Entropy Clustering Algorithms in Image Segmentation

Abstract

Clustering or data grouping presents fundamental initial procedure in image processing. This paper addresses the problem of combining the concept of rough sets and entropy measure in the area of image segmentation. In the present study, comprehensive investigation into rough set entropy based thresholding image segmentation techniques has been performed. Segmentation presents the low-level image transformation routine concerned with image partitioning into distinct disjoint and homogenous regions with thresholding algorithms most often applied in practical solutions when there is pressing need for simplicity and robustness. Simultaneous combining entropy based thresholding with rough sets results in rough entropy thresholding algorithm. In the present paper, new algorithmic schemes Standard RECA (Rough Entropy Clustering Algorithm) and Fuzzy RECA in the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering model taking advantage of dealing with some degree of uncertainty in analyzed data. Both Standard and Fuzzy RECA algorithmic schemes performed usually equally robustly compared to standard k-means algorithm. At the same time, in many runs yielding slightly better performance making possible future implementation in clustering applications.