64519 eng Bhoyar, Kishor Colour Image Segmentation using Fast Fuzzy C-Means Algorithm This paper proposes modified FCM (Fuzzy C-Means) approach to colour image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given colour image is computed using JND colour model. This samples the colour space so that just enough number of histogram bins are obtained 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. The proposed fast FCM(FFCM) algorithm 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 criteria namely PSNR(Peak Signal to Noise Ratio) and PRI (Probabilistic Rand Index) are used to evaluate the performance. Although 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 Article de fons Colour Image Segmentation JND Histogram Fuzzy C-means Clustering Fast FCM Segmentación de imágenes en color Histograma JND Segmentació d'imatges en color Kakde, Omprakash Vol. 9, Núm. 1 ( 2010), p. 18-31 ELCVIA : Electronic Letters on Computer Vision and Image Analysis 1577-5097 14 327105 http://ddd.uab.cat/pub/elcvia/15775097v9n1p18.pdf 0018 31 1 9 15775097v9n1 2010 ARTPUB info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Tots els drets reservats http://www.europeana.eu/rights/rr-f/