Abstract
Color quantization is an important operation with many applications in graphics and image processing. Clustering methods based on the competitive learning paradigm, in particular self-organizing maps, have been extensively applied to this problem. In this paper, we investigate the performance of the batch neural gas algorithm as a color quantizer. In contrast to self-organizing maps, this competitive learning algorithm does not impose a fixed topology and is insensitive to initialization. Experiments on publicly available test images demonstrate that, when initialized by a deterministic preclustering method, the batch neural gas algorithm outperforms some of the most popular quantizers in the literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Brun, L., Trémeau, A.: Color Quantization. In: Digital Color Imaging Handbook, pp. 589–638. CRC Press (2002)
Heckbert, P.: Color Image Quantization for Frame Buffer Display. ACM SIGGRAPH Comp. Graph 16, 297–307 (1982)
Gervautz, M., Purgathofer, W.: A Simple Method for Color Quantization: Octree Quantization. In: New Trends in Computer Graphics, pp. 219–231. Springer (1988)
Wan, S., et al.: Variance-Based Color Image Quantization for Frame Buffer Display. Color Res. Appl. 15, 52–58 (1990)
Wu, X.: Efficient Statistical Computations for Optimal Color Quantization. In: Graphics Gems, vol. II, pp. 126–133. Academic Press (1991)
Joy, G., Xiang, Z.: Center-Cut for Color Image Quantization. Visual Comput. 10, 62–66 (1993)
Yang, C.Y., Lin, J.C.: RWM-Cut for Color Image Quantization. Comput Graph 20, 577–588 (1996)
Velho, L., et al.: Color Image Quantization by Pairwise Clustering. In: Proc SIBGRAPI, pp. 203–210 (1997)
Brun, L., Mokhtari, M.: Two High Speed Color Quantization Algorithms. In: Proc CGIP, pp. 116–121 (2000)
Xiang, Z.: Color Image Quantization by Minimizing the Maximum Intercluster Distance. ACM T. Graphic 16, 260–276 (1997)
Celebi, M.E.: Improving the Performance of K-means for Color Quantization. Image Vision Comput 29, 260–271 (2011)
Wen, Q., Celebi, M.E.: Hard vs. Fuzzy C-Means Clustering for Color Quantization. Eurasip. J. Adv. Sig. Pr. 2011(1), 118–129 (2011)
Dekker, A.: Kohonen Neural Networks for Optimal Colour Quantization. Network- Comp. Neural 5, 351–367 (1994)
Cottrell, M., et al.: Batch and Median Neural Gas. Neural Networks 19, 762–771 (2006)
Martinetz, T., et al.: Neural-Gas Network for Vector Quantization and its Application to Time-Series Prediction. IEEE T. Neural Networ. 4, 558–569 (1993)
Cheng, S., Yang, C.: Fast and Novel Technique for Color Quantization Using Reduction of Color Space Dimensionality. Pattern Recogn. Lett. 22, 845–856 (2001)
Franzen, R.W.: Kodak Lossless True Color Image Suite (1999), http://www.r0k.us/graphics/kodak/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Celebi, M.E., Wen, Q., Schaefer, G., Zhou, H. (2012). Batch Neural Gas with Deterministic Initialization for Color Quantization. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)