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Application of Information Redundancy Measure To Image Segmentation

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Book cover Intelligent Data Processing (IDP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 794))

Abstract

In this paper, the problem of image segmentation quality is considered. The main idea is to find a quality criterion, which could have an extremum. The problem is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. We propose to use information redundancy measure as a criterion for optimizing segmentation quality. The method for constructing the redundancy measure provides criterion with extremal properties. To show efficiency of the proposed criterion, computing experiment is carried out. The proposed criterion is combined with SLIC and EDISON segmentation algorithms. Computing experiment shows that the segmented image corresponding to a minimum of redundancy measure produces acceptable information distance when compared with the original image. In most cases, the lowest information distance between this segmented image and ground-truth segmentations is obtained. An example of applying the redundancy measure to segmentation of images of painting material cross-sections is considered.

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Acknowledgements

The research was supported in part by the Russian Foundation for Basic Research (grants No. 18-07-01385 and No. 18-07-01231).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Dmitry Murashov

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  1. Dmitry Murashov
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Correspondence to Dmitry Murashov .

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Editors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Vadim V. Strijov

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Yandex School of Data Analysis, Moscow, Russia

    Konstantin V. Vorontsov

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Murashov, D. (2019). Application of Information Redundancy Measure To Image Segmentation. In: Strijov, V., Ignatov, D., Vorontsov, K. (eds) Intelligent Data Processing. IDP 2016. Communications in Computer and Information Science, vol 794. Springer, Cham. https://doi.org/10.1007/978-3-030-35400-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-35400-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35399-5

  • Online ISBN: 978-3-030-35400-8

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