Abstract
With the explosive growth of the World Wide Web and rapidly growing number of available digital color images, much research effort is devoted to the development of efficient content-based image retrieval systems. In this paper we propose to apply the Gaussian Mixture Model for color image indexing. Using the proposed approach, the color histograms are being modelled as a sum of Gaussian distributions and their parameters serve as signatures, which provide for fast and efficient color image retrieval. The results of the performed experiments show that the proposed approach is robust to color image distortions introduced by lossy compression artifacts and therefore it is well suited for indexing and retrieval of Internet based collections of color images stored in lossy compression formats.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Smolka, B., Szczepanski, M., Lukac, R., Venetsanoloulos, A.: Robust color image retrieval for the World Wide Web. In: Proceedings of ICASSP, pp. 461–464 (2004)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data. J. Royal Stat. Soc. 39, 1–38 (1997)
Bilmes, J.: A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report ICSI-TR-97-021, University of Berkeley (1997)
Wu, J.: On the convergence properties of the EM algorithm. The Annals of Statistics 11(1), 95–103 (1983)
Xu, L., Jordan, M.I.: On convergence properties of the EM algorithm for Gaussian mixtures. Neural Computation 8, 129–151 (1996)
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. Pattern Analysis and Machine Intelligence, IEEE Transactions on 23, 947–963 (2001)
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vision 7, 11–32 (1991)
Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: ICCV, pp. 59–66 (1998)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R.: Image indexing using color correlograms. In: Computer Vision and Pattern Recognition, 1997 IEEE Computer Society Conference, pp. 762–768. IEEE Computer Society Press, Los Alamitos (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luszczkiewicz, M., Smolka, B. (2007). Gaussian Mixture Model Based Retrieval Technique for Lossy Compressed Color Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-74260-9_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74258-6
Online ISBN: 978-3-540-74260-9
eBook Packages: Computer ScienceComputer Science (R0)