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30 March 2000 Tuning on the fly of structural image analysis algorithms using data mining
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In image reconnaissance the analyst of remotely sensed imagery is confronted with large amounts of data. Especially the integration of multi-sensor data calls for support of the observer by automatic image processing algorithms. For this purpose we recently developed model based structural image analysis algorithms which deliver successful results. However, varying scenarios, different applications and changing image material often require a tuning of the algorithms. Therefore, we suggest techniques to support and automate the adaptation of the image processing to changing requirements. Our approach uses techniques form data mining to discover relationships between image properties and optimal parameter vectors. This paper addresses two points: a supervised tuning approach and suggestions for unsupervised tuning. For the supervised tuning a representative image database was set up, and a corresponding ground truth was interactively defined. The results of the structural image analysis for a set of parameters can be compared to the ground truth. For the example images the parameters were optimized using an evolutionary optimization loop. For the unsupervised tuning the data form the supervised optimization is analyzed. We present promising results form manual clustering and propose a clustering approach based on decision trees, and hierarchical and evolutionary cluster algorithms with different distance measures.
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Aljoscha Klose, Rudolf Kruse, Hermann Gross, and Ulrich Thoennessen "Tuning on the fly of structural image analysis algorithms using data mining", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000);

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