Abstract
Information mining opens new perspectives and a huge potential for information extraction from large volumes of heterogeneous images and the correlation of this information with the goals of applications. We present a new concept and system for image information mining, based on modelling the causalities which link the image-signal contents to the objects and structures within interest of the users. The basic idea is to split the information representation into four steps:
1. image feature extraction using a library of algorithms so as to obtain a quasi-complete signal description
2. unsupervised grouping in a large number of clusters to be suitable for a large set of tasks
3. data reduction by parametric modelling the clusters
4. supervised learning of user semantics, that is the level where, instead of being programmed, the systems is trained by a set of examples; thus the links from image contents to the users are created.
The record of the sequence of links is a knowledge acquisition process, the system memorizes the user hypotheses. Step 4. is a man-machine dialogue, the information exchange is done using advanced visualization tools. The system learns what the users need. The system is presently prototyped for inclusion in a new generation of intelligent satellite ground segment systems, value adding tools in the area of geoinformation, and several applications in medicine and biometrics are also foreseen.
Keywords
- Synthetic Aperture Radar
- Relevance Feedback
- Interactive Learning
- Content Base Image Retrieval
- User Interest
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Cox, I.J., Miller, M.L., Omohundro, S.M., Yianilos, P.N.: PicHunter: Bayesian Relevance Feedback for Image Retrieval. In: Proc. Int. Conf. on Pattern Recognition, Vienna, Austria (1996)
Datcu, M., Seidel, K., Walessa, M.: Spatial Information Retrieval From Remote Sensing Images: Part I. Information Theoretical Perspective. IEEE Tr. On Geoscience and Remote Sensing 36, 1431–1445 (1998)
Datcu, M., Seidel, K., Schwarz, G.: Elaboration of advanced tools for information retrieval and the design of a new generation of remote sensing ground segment systems. In: Kanellopoulos, I. (ed.) Machine Vision in Remote Sensing, pp. 199–212. Springer, Heidelberg (1999)
Datcu, M., Seidel, K.: Bayesian methods: applications in information aggregation and data mining. International Archives of Photogrammetry and Remote Sensing 32, Part 7-4-3 W6, 68–73 (1999)
Datcu, M., Seidel, K., D’Elia, S., Marchetti, P.G.: Knowledge-driven Information-Mining in remote sensing image archives. ESA Bulletin (110), 26–33 (2002)
Schröder, M., Rehrauer, H., Seidel, K., Datcu, M.: Spatial Information Retrieval From Remote Sensing Images: Part II. Gibbs Markov Random Fields. IEEE Tr. on Geoscience and Remote Sensing 36, 1446–1455 (1998)
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, 2288–2298 (2000)
Minka, T.P., Picard, R.W.: Interactive learning with a society of models. Pattern Recognition 30, 565–581 (1997)
Rehrauer, H., Seidel, K., Datcu, M.: Multi-scale indices for contentbased image retrieval. In: Proc. of 1999 IEEE International Geoscience and Remote Sensing Symposium IGARSS 1999, vol. V, pp. 2377–2379 (1999)
Rehrauer, H., Datcu, M.: Selecting scales for texture models. In: Pietikäinen, M.K. (ed.) Texture analysis in machine vision. Series in machine perception and artificial intelligence, vol. 40. World Scientific, Singapore (2000)
Veltkamp, C.R., Burkhardt, H., Kriegel, H.-P. (eds.): State-of-the-Art in Content-Based Image and Video Retrieval. Kluwer, Dordrecht (2001)
Zhang, J., Hsu, W., Lee, M.L.: Image Mining: Issues, Frameworks and Techniques. In: Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD 2001), San Francisco, CA, USA (August 2001)
Datcu, M., Seidel, K., D’Elia, S., Marchetti, P.G.: Knowledge Driven Information Mining in Remote Sensing Image Archives. ESA Bulletin 110, 26–33 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Datcu, M., Seidel, K. (2003). An Innovative Concept for Image Information Mining. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds) Mining Multimedia and Complex Data. PAKDD 2002. Lecture Notes in Computer Science(), vol 2797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39666-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-39666-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20305-6
Online ISBN: 978-3-540-39666-6
eBook Packages: Springer Book Archive