Focused Image Color Quantization using Magnitude Sensitive Competitive Learning Algorithm

Enrique Pelayo, David Buldain, Carlos Orrite

2012

Abstract

This paper introduces the Magnitude Sensitive Competitive Learning (MSCL) algorithm for Color Quantization. MSCL is a neural competitive learning algorithm, including a magnitude function as a factor of the measure used for the neuron competition. This algorithm has the property of distributing color vector prototypes in certain data-distribution zones according to an arbitrary magnitude locally calculated for every unit. Therefore, it opens the possibility not only to distribute the codewords (colors of the palette) according to their frequency, but also to do it in function of any other data-dependent magnitude focused on a given task. This work shows some examples of focused Color Quantization where the objective is to represent with high detail certain regions of interest in the image (salient area, center of the image, etc.). The oriented behavior of MSCL permits to surpass other standard Color Quantization algorithms in these tasks.

References

  1. Ahalt, S., Krishnamurthy, A., Chen, P., and Melton, D. (1990). Competitive learning algorithms for vector quantization. Neural networks, 3(3):277-290.
  2. Atsalakis, A. and Papamarkos, N. (2006). Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas. Engineering Applications of Artificial Intelligence, 19(7):769-786.
  3. Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press.
  4. Bishop, C., Svensén, M., and Williams, C. (1998). GTM: The generative topographic mapping. Neural computation, 10(1):215-234.
  5. Celebi, M. (2009). An effective color quantization method based on the competitive learning paradigm. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV, volume 2, pages 876-880.
  6. Celebi, M. (2011). Improving the Performance of KMeans for Color Quantization. Image (Rochester, N.Y.), 29(4):260-271.
  7. Celebi, M. and Schaefer, G. (2010). Neural Gas Clustering for Color Reduction. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV, volume 1, pages 429-432.
  8. Chang, C., Xu, P., and Xiao, R. (2005). New adaptive color quantization method based on self-organizing maps. Neural Networks, IEEE, 16(1):237-249.
  9. Cheng, G., Yang, J., Wang, K., and Wang, X. (2006). Image Color Reduction Based on Self-Organizing Maps and Growing Self-Organizing Neural Networks. 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06), pages 24-24.
  10. Dekker, A. (1994). Kohonen Neural Networks for Optimal Colour Quantization. Network: Computation in Neural Systems, 3(5):351-367.
  11. Durbin, R. and Willshaw, D. (1987). An analogue approach to the travelling salesman problem using an elastic net method. Nature, 326(6114):689-691.
  12. Itti, L. and Koch, C. (2001). Computational modeling of visual attention. Nature reviews neuroscience, 2(3):194-203.
  13. Koch, C. and Ullman, S. (1985). Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4(4):219-227.
  14. Kohonen, T. (2001). Self-Organizing Maps. Springer.
  15. Lazaro, J., Arias, J., Martin, J., Zuloaga, A., and Cuadrado, C. (2006). SOM Segmentation of gray scale images for optical recognition. Pattern Recognition Letters, 27(16):1991-1997.
  16. Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2):129-137.
  17. Martinetz, T., Berkovich, S., and Schulten, K. (1993). 'Neural-gas' network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4):558-569.
  18. Nikolaou, N. and Papamarkos, N. (2009). Color reduction for complex document images. International Journal of Imaging Systems and Technology, 19(1):14-26.
  19. Papamarkos, N. (2003). A neuro-fuzzy technique for document binarisation. Neural Computing and Applications, 12(3-4):190-199.
  20. Pelayo, E., Buldain, D., and Orrite, C. (2012). Magnitude Sensitive Competitive Learning. In 20th European Symposium on Artificial Neural Networks, Comp. Int. and Machine Learning, volume 1, pages 305-310.
  21. Treisman, A. and Gelade, G. (1980). A feature integration theory of attention. Cognitive Psychology, 12:97-136.
  22. Uchiyama, T. and Arbib, M. (1994). Color image segmentation using competitive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(12):1197-1206.
  23. Vazquez, E., Gevers, T., Lucassen, M., van de Weijer, J., and Baldrich, R. (2010). Saliency of color image derivatives: a comparison between computational models and human perception. Journal of the Optical Society of America. A, Optics, image science, and vision, 27(3):613-21.
  24. Xu, L., Krzyzak, A., and Oja, E. (1993). Rival Penalized Competitive Learning for Clustering Analysis, RBF net and Curve Detection. IEEE Tr. on Neural Networks, 4:636-649.
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Paper Citation


in Harvard Style

Pelayo E., Buldain D. and Orrite C. (2012). Focused Image Color Quantization using Magnitude Sensitive Competitive Learning Algorithm . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 516-521. DOI: 10.5220/0004150805160521


in Bibtex Style

@conference{ncta12,
author={Enrique Pelayo and David Buldain and Carlos Orrite},
title={Focused Image Color Quantization using Magnitude Sensitive Competitive Learning Algorithm},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={516-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004150805160521},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Focused Image Color Quantization using Magnitude Sensitive Competitive Learning Algorithm
SN - 978-989-8565-33-4
AU - Pelayo E.
AU - Buldain D.
AU - Orrite C.
PY - 2012
SP - 516
EP - 521
DO - 10.5220/0004150805160521