Rough C-means and Fuzzy Rough C-means for Colour Quantisation

• Autorzy: Schaefer G. , Hu Q. , Zhou H. , Peters J.F. , Hassanien A. E.
• Języki publikacji: EN

Abstrakty

EN

Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly deter- mines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper we propose rough c-means and fuzzy rough c-means clustering algorithms for colour quantisation of images. Both approaches utilise the concept of lower and upper approximations of clusters to define palette colours. While in the rough c-means approach cluster centroids are refined iteratively through a linear combination of elements of the lower and upper approximations, the fuzzy rough c-means technique assigns variable membership values to the elements in the boundary region which in turn are incorporated into the calculation of cluster centres. Experimental results on a standard set of images show that these approaches performs significantly better than other, purpose built colour quantisation algorithms.

Słowa kluczowe

EN

colour quantisation; clustering; rough c-means

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2012
• Tom: Vol. 119, nr 1
• Strony: 113--120
• Opis fizyczny: Bibliogr. 12 poz., fot., tab.

Twórcy

autor

Schaefer G.

autor

Hu Q.

autor

Zhou H.

autor

Peters J.F.

autor

Hassanien A. E.

• Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, U.K,

Bibliografia

• [1] Bezdek, J.: A convergence theorem for the fuzzy isodata clustering algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, 2, 1980, 1-8.
• [2] Chuang, K., Tzeng, S., Chen, H., Wu, J., Chen, T.: Fuzzy C-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics, 30, 2006, 9-15.
• [3] Dekker, A.: Kohonen neural networks for optimal colour quantization, Network: Computation in Neural Systems, 5, 1994, 351-367.
• [4] Gervautz, M., Purgathofer, W.: A Simple Method for Color Quantization: Octree Quantization, in: Graphics Gems (A. Glassner, Ed.), 1990, 287-293.
• [5] Heckbert, P. S.: Color Image Quantization for Frame Buffer Display, ACM Computer Graphics (ACM SIGGRAPH '82 Proceedings), 16(3), 1982, 297-307.
• [6] Hu, Q., Yu, D.: An improved clustering algorithm for information granulation, 2nd Int. Conference on Fuzzy Systems and Knowledge Discovery, 2005.
• [7] Lingras, P., West, C.: Interval set clustering of web users with rough k-means, Journal Intell. Inform. Syst., 23, 2004, 5-16.
• [8] Nolle, L., Schaefer, G.: Color map design through optimization, Engineering Optimization, 39(3), 2007, 327-343.
• [9] Pawlak, Z.: Rough sets, Int. Journal Inform. Comput. Sci, 11, 1982, 145-172.
• [10] Peters, G.: Some refinements of rough k-means clustering, Pattern Recognition, 39, 2006, 1481-1491.
• [11] Scheunders, P.: A genetic c-means clustering algorithm applied to color image quantization, Pattern Recognition, 30(6), 1997, 859-866.
• [12] Yao, Y., Li, X., Lin, T., Liu, Q.: Representation and classification of rough set models, 3rd Int. Workshop on Rough Sets and Soft Computing, 1994.