Abstract
Rate distortion theory is one of the areas of information transmission theory with important applications in multimodal signal processing, as for example image processing, information bottleneck and steganalysis. This article present an image characterization method based on rate distortion analysis in the feature space. This space is coded using clustering as vector quantization (k-means). Since image information usually cannot be coded by single clusters, because there are image regions corresponding to groups of clusters, the rate and distortion are specifically defined. The rate distortion curve is analyzed, extracting specific features for implementing a database image classification system.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Datcu, M., Seidel, K., D’Elia, S., Marchetti, P.G.: Knowledge - driven information mining in remote sensing image archives. ESA bulletin 110 (2002)
Schröder, M., Rehrauer, H., Seidel, K., Datcu, M.: Interactive learning and probabilistic retrieval in remote sensing image archives. IEEE Trans. on Geoscience and Remote Sensing 38(5), 2288–2298 (2000)
Faur, D., Gavat, I., Datcu, M.: Mutual Information based measures for image content characterization. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 342–349. Springer, Heidelberg (2005)
Celik, M.U., Sharma, G., Tekalp, A.M.: Universal image steganalysis using ratedistortion curves. In: Proc. SPIE: Security, Steganography and Watermarking of Multimedia Contents VI, San Jose, USA, vol. 5306, pp. 19–22 (2004)
Goldberger, J., Greenspan, H., Gordon, S.: Unsupervised Image Clustering using the Information Bottleneck Method. In: The Annual Pattern Recognition Conference DAGM, Zurich (2002)
Tasto, M., Wintz, P.: A bound on the rate-distortion function and application to images. IEEE Transactions on Information Theory 18(1), 150–159 (1972)
Schröder, M., Walessa, M., Rehrauer, H., Seidel, K., Datcu, M.: Gibbs random field models: a toolbox for spatial information extraction. Computers and Geosciences 26, 423–432 (2000)
Schröder, M., Rehrauer, H., Seidel, K., Datcu, M.: Spatial information retrieval from remote sensing images: Part II Gibbs Markov Random Field. IEEE Trans. On Geoscience and Remote Sensing 36, 1446–1455 (1998)
Datcu, M., Stoichescu, D.A., Seidel, K., Iorga, C.: Model fitting and model evidence for multiscale image texture analysis. In: American Institute of Physics, AIP Conference Proceedings, vol. 735, pp. 35–42 (2004)
Sugar, C.A., James, G.M.: Finding the Number of Clusters in a Data Set: An Information Theoretic Approach. Journal of the American Statistical Association 98, 750–763 (2003)
Blahut, E.R.: Computation of Channel Capacity and Rate-Distortion Functions. IEEE transactions on Information Theory IT-18(4) (1972)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iancu, C., Gavat, I., Datcu, M. (2006). Image Disorder Characterization Based on Rate Distortion. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_28
Download citation
DOI: https://doi.org/10.1007/11881216_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45914-9
Online ISBN: 978-3-540-45915-6
eBook Packages: Computer ScienceComputer Science (R0)