Application of information-theoretical approach for image segmentation
A problem of segmentation quality of digital images is considered. The developed technique is based on the information-theoretical approach and applied to a modiﬁed superpixel segmentation algorithm.In one ofthe conventional techniques, the weighted uncertaintyindexis usedfor measuring segmentation quality. The index is calculated using normalized mutual information of color channels in given and segmented images. The uncertainty index varies monotonously depending on the parameter of the segmentation algorithm. This caused application of learning technique and iterative procedure for choosing parameter value. In this work, information redundancy measure is proposed as a criterion for optimizing segmentation quality. This criterion provides the best result in terms of visual perception. It is shown that proposed method of constructing the redundancy measure provides it with extremal properties. The experiment was conducted using the images from the database Berkeley Segmentation Dataset. The experiment conﬁrmed that the segmented image corresponding to a minimum of redundancy measure produces the minimum diﬀerence in the information-theoretical dissimilarity measure when compared with the original image. In addition, the segmented image that was selected using the proposed criteria, gives the highest similarity with the groundtruth segmentations, available in the database.