Two-dimensional clustering algorithms for image segmentation
Intan Aidha Yusoff, Nor Ashidi Mat Isa · WSEAS Transactions on Computers archive · 2011
This paper introduces modified versions of the K-Means (KM) and Moving K-Means (MKM) clustering algorithms, called the Two-Dimensional K-Means (2D-KM) and Two-Dimensional Moving K-Means (2D-MKM) algorithms respectively. The performances of these two proposed algorithms are compared with three of the commonly used conventional clustering algorithms, namely K-Means (KM), Fuzzy C-Means (FCM), and Moving K-Means (MKM). The new algorithms incorporate the median value of considered pixel intensity with its neighboring pixel; together with the pixel's own intensity for the assigning process of the pixel to the nearest cluster. From the observed qualitative and quantitative results, it is proven that 2D-KM and 2D-MKM perform better than KM, FCM, and MKM in terms of producing more homogeneous segmentation results, while taking shorter time in executing the process as compared to FCM.