Abstract
In this paper knowledge based image interpretation is formulated and solved as an optimization problem which takes into account the observed image data, the available task specific knowledge, and the requirements of an application. Knowledge is represented by a semantic network consisting of concepts (nodes) and links (edges). Concepts are further defined by attributes, relations, and a judgment function. The interface between the symbolic knowledge base and the results of image (or signal) processing and initial segmentation is specified via primitive concepts.
We present a recently developed approach to optimal interpretation that is based on the automatic conversion of the concept oriented semantic network to an attribute centered representation and the use of iterative optimization procedures, like e.g. simulated annealing or genetic algorithms. We show that this is a feasible approach which provides ‘any-time’ capability and allows parallel processing. It provides a well-defined combination of signal and symbol oriented processing by optimizing a heuristic judgment function.
The general ideas have been applied to various problems of image and speech understanding. As an example we describe the recognition of streets from TV image sequences to demonstrate the efficiency of iterative optimization.
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Niemann, H., Fischer, V., Paulus, D., Fischer, J. (1996). Knowledge based image understanding by iterative optimization. In: Görz, G., Hölldobler, S. (eds) KI-96: Advances in Artificial Intelligence. KI 1996. Lecture Notes in Computer Science, vol 1137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61708-6_68
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DOI: https://doi.org/10.1007/3-540-61708-6_68
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