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.
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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
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DOI: https://doi.org/10.1007/978-3-540-39666-6_6
Publisher Name: Springer, Berlin, Heidelberg
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