Color Image Segmentation using Fast Fuzzy C-Means Algorithm - Kishor Keshaorao Bhoyar; Omprakash G. kakde (ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2010)
This paper proposes modified FCM (Fuzzy C-means) approach to color image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given color image is computed using JND color model. This samples the color space so that just enough number of histogram bins are obtained on each axis without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. Then the modified FCM algorithm is proposed that works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criterias namely PSNR and PRI (Probabilistic Rand Index) are used to evaluate the performance. Results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm in terms of PSNR and PRI.