Image segmentation based on adaptive cluster prototype estimation

An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.

I. Introduction

Image segmentation is an important low-level preprocessing step for many computer vision problems [1], [2]. Many algorithms for image segmentation using fuzzy techniques have been proposed [3]–[6]. One popular technique involves using the fuzzy c-means (FCM) algorithm [7], or variants of it, to compute the membership values for different classes before the final segmentation. The FCM algorithm assigns membership value to a data sample based on its proximity to the cluster prototypes in the feature space. In the FCM-based segmentation algorithm, feature vectors are assumed to be independent of each other and independent of their spatial coordinates.