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Image Information Mining System Evaluation Using Information-Theoretic Measures


During the last decade, the exponential increase of multimedia and remote sensing image archives, the fast expansion of the world wide web, and the high diversity of users have yielded concepts and systems for successful content-based image retrieval and image information mining. Image data information systems require both database and visual capabilities, but there is a gap between these systems. Database systems usually do not deal with multidimensional pictorial structures and vision systems do not provide database query functions. In terms of these points, the evaluation of content-based image retrieval systems became a focus of research interest. One can find several system evaluation approaches in literature, however, only few of them go beyond precision-recall graphs and do not allow a detailed evaluation of an interactive image retrieval system. Apart from the existing evaluation methodologies, we aim at the overall validation of our knowledge-driven content-based image information mining system. In this paper, an evaluation approach is demonstrated that is based on information-theoretic quantities to determine the information flow between system levels of different semantic abstraction and to analyze human-computer interactions.

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Correspondence to Herbert Daschiel.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Daschiel, H., Datcu, M. Image Information Mining System Evaluation Using Information-Theoretic Measures. EURASIP J. Adv. Signal Process. 2005, 941483 (2005).

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