Abstract
Color image quantization (CIQ) is one of the important and well-accepted application areas in the field of data compression where a truly colored image is mainly represented by less number of selected significant color pixels. CIQ is performed in two major phases, i.e., color palette design and pixel mapping. The performance of any CIQ depends on the construction of a proper color palette, and this construction process is computationally expensive. In this paper, we have proposed a color palette design algorithm where we have incorporated two different types of clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and K-means. Initially, we have decomposed the color image into several non-overlapping blocks, and subsequently, we have employed DBSCAN on each block. This process has concerned for some sort of initial screening of representative color pixels. Further, we have obtained the desired size of color palette, employing K-means on the earlier selected representative color pixels. We have tested the proposed scheme on a set of benchmark test images and obtained the satisfactory results in terms of the visual quality of the reconstructed images. In case of designing the color palette, the proposed scheme requires less computational time compare with the conventional K-means algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Orchard, M.T., Bouman, C.A.: Color quantization of images. IEEE Trans. Signal Process. 39(12), 2677–2690 (1991)
Gan, G., Ma, C., Wu, J.: Data clustering: theory. In: Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability), SIAM, 2007
Bottou, L., Bengio, Y.: Convergence properties of the K-means algorithms. In: Advances in Neural Information Processing Systems, vol 7, pp. 585–592. MIT Press (1995)
Likasa, A., Vlassisb, N., Verbeekb, J.J.: The global K-means clustering algorithm. Pattern Recogn. 36, 451–461 (2003)
Na, S.: Research on K-means clustering algorithm: an improved K-means clustering algorithm. In: Intelligent Information Technology and Security Informatics (IITSI), pp. 63–67. (2010)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient K-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Emre Celebi, M.: Improving the performance of K-means for color quantization. Image Vis. Comput. 29(4), 260–271 (2010)
Emre Celebi, M., Kingravi, H.A., Vela, A.P.: A comparative study of efficient initialization methods for the K-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2012)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), (1996)
Mumtaz, K., Duraiswamy, K.: A novel density based improved K-means clustering algorithm-Dbkmeans. Int. J. Comput. Sci. Eng. 02(02), 213–218 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Rahul, K., Agrawal, R., Pal, A.K. (2014). Color Image Quantization Scheme Using DBSCAN with K-Means Algorithm. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_106
Download citation
DOI: https://doi.org/10.1007/978-81-322-1665-0_106
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
Online ISBN: 978-81-322-1665-0
eBook Packages: EngineeringEngineering (R0)