Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning
Jun Zhang and Jinglu Hu
Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.
-  J. P. Braquelaire and L. Brun, “Comparison and Optimization of Methods of Color Image Quantization,” IEEE Trans. Image Process., Vol.6, No.7, pp. 1048-1052, Jul. 1997.
-  M. T. Orchard and C. A. Bouman, “Color Quantization of Image,” IEEE Trans. Signal Process., Vol.39, No.12, pp. 2677-2690, Dec. 1991.
-  N. Papamarkos, A. E. Atsalakis, and C. P. Strouthopoulos, “Adaptive Color Reduction,” IEEE Trans. Syst., Man, Cybern. B, Vol.32, No.1, pp. 44-56, 2002.
-  P. Heckbert, “Color Image Quantization for Frame Buffer Display,” Comput. Graph., Vol.16, pp. 297-307, 1982.
-  W. D. Chen and W. Ding, “An Improved Median-Cut Algorithm of Color Image Quantization,” Int. Conf. Computer Science and Software Engineering, pp. 943-946, Dec. 2008.
-  I. Ashdown, “Octree Color Quantization,” in Radiosity A Programmers Perspective, New York: Wiley, 1994.
-  B. L. Guo and X. Fu, “A Modified Octree Color Quantization Algorithm,” Int. Conf. Communications and Networking, pp. 1-3, Oct. 2006.
-  S. J. Wan, P. Prusinkiewicz, and S. K. M. Wong, “Variance Based Color Image Quantization for Frame Buffer Display,” Color Res. Applicat., Vol.15, No.1, pp. 52-58, 1990.
-  R. Hecht-Nielsen, “Counterpropagation Networks,” Appl. Opt., Vol.26, pp. 4979-4984, 1987.
-  C. Chang, P. F. Xu, R. Xiao, and T. Srikanthan, “New Adaptive Color Quantization Method Based on Self-Organizing Maps,” IEEE Trans. Neural Netw., Vol.16, No.1, pp. 237-249, 2005.
-  T. M. Martinez, S. G. Berkovich, and K. J. Schulten, “Neural-Gas Network for Vector Quantization and its Application to Time-Series Prediction,” IEEE Trans. Neural Netw., Vol.4, No.4, pp. 558-568, Jul. 1993.
-  C. Theoharatos, G. Economou, S. Fotopoulos, and N. A. Laskaris, “Color-Based Image Retrieval Using Vector Quantization and Multivariate Graph Matching,” IEEE Int. Conf. Image Processing, Vol.1, pp. 537-540, Sept. 2005.
-  F. L. Chung and T. Lee, “Fuzzy Competitive Learning,” Neural Netw., Vol.7, No.3, pp. 539-551, 1994.
-  A. K. Krishnamurthy, S. C. Ahalt, D. E. Melton, and P. Chen, “Neural Networks for VQ of Speech and Images,” IEEE J. Sel. Areas Commun., Vol.38, No.1, pp. 25-29, Jan. 1992.
-  H. Frigui and R. Krishnapuram, “A Robust Competitive Clustering Algorithm with Applications in Computer Vision,” IEEE Trans. Pattern Anal. Machine Intell., Vol.21, pp. 450-465, May 1999.
-  C. De Stefano, C. D’Elia, and A. Marcelli, “A Dynamic Approach to Learning Vector Quantization,” 17th Int. Conf. Pattern Recognition, Vol.4, pp. 601-604, Aug. 2004.
-  B. Everitt, S. Landau, and M. Leese, “Cluster Analysis,” London: Arnold, 2001.
-  G. McLachlan and D. Peel, “Finite Mixture Models,” New York: Wiley, 2000.
-  M. Girolami, “Mercer Kernel Based Clustering in Feature Space,” IEEE Trans. Neural Netw., Vol.13, No.3, pp. 780-784, May 2002.
-  B. Fritzke, “Growing Cell Structures A Self-Organizing Network for Unsupervised and Supervised Learning,” Neural Networks, Vol.7, No.9, pp. 1441-1460, 1994.
-  J. A. F. Costa and R. S. Oliveira, “Cluster Analysis Using Growing Neural Gas and Graph Partitioning,” Int. Joint Conf. Neural Networks, pp. 3051-3056, Aug. 2007.
-  T. Riemersma, “Color Metric,”
[Online] Available: http://www.compuphase.com/cmetric.htm.
-  G. Dong and M. Xie, “Color Clustering and Learning for Image Segmentation Based on Neural Networks,” IEEE Trans. Neural Netw., Vol.16, No.4, pp. 925-936, Jul. 2005.
-  S. C. Pei and Y. S. Lo, “Color Image Compression and Limited Display Using Self-Organization Kohonen Map,” IEEE Trans. Circuits Syst. Video Technol., Vol.8, No.2, pp. 191-205, Apr. 1998.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.