Approach to image segmentation based on interval type-2 fuzzy subtractive clustering

The paper deals with an approach to image segmentation using interval type-2 fuzzy subtractive clustering (IT2-SC). The IT2-SC algorithm is proposed based on extension of subtractive clustering algorithm (SC) with fuzziness parameter m. And to manage uncertainty of the parameter m, we have expanded the SC algorithm to interval type-2 fuzzy subtractive clustering (IT2-SC) using two fuzziness parameters m 1 and m 2 which creates a footprint of uncertainty (FOU) for the fuzzifier. The input image is extracted RGB values as input space of IT2-SC; number of clusters is automatically identified based on parameters of the algorithm and image properties. The experiments of image segmentation are implemented in variety of images with statistics. © 2012 Springer-Verlag.