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In this paper, a problem of image segmentation quality is considered. The problem of segmentation quality is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. We use superpixel algorithm SLIC supplemented with the simple postprocessing procedure for generating a set of partitioned images with different number of segments. A technique for selecting the best segmented image is proposed. We propose to use information redundancy measure as a criterion for optimizing segmentation quality. It is shown that proposed method for constructing the redundancy measure provides it with extremal properties. Computing experiment was conducted using the images from the Berkeley Segmentation Dataset. The experiment confirmed that the segmented image corresponding to a minimum of redundancy measure produces the suitable dissimilarity when compared with the original image. The segmented image that was selected using the proposed criterion, gives the highest similarity with the ground-truth segmentations, available in the database.

Segmentation Technique based on Information Redundancy Minimization