METHOD FOR COMBINING IMAGE SEGMENTATION MAPS BASED ON MINIMIZING INFORMATION REDUNDANCY AND VARIATION OF INFORMATION
D.M. Murashov
Federal Research Center "Computer Science and Control", Russian Academy of Sciences, Moscow, Russia
Keywords: image segmentation, image partition, combining segmentation maps, measure of information redundancy, variation of information
Abstract
In this paper, we propose a new two-level method for combining image segmentation maps based on minimizing the two-objective quality functional. The functional is formed as a weighted sum of the information redundancy measure and variation of information computed from the original image and the combined segmentation map. Applying such a measure, we obtain an image partition that provides a compromise between the objectives of minimizing the number of outlined informationally important segments and minimizing the information difference between the original image and the resulting partition. The proposed method improves the result of segmentation in comparison with the method for combining partitions based on the criterion of the minimum information redundancy.
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