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Author: Dmitry Murashov

Affiliation: Federal Research Center “Computer Science and Control” of RAS, Russian Federation

Keyword(s): Image Segmentation, Segmentation Quality, Redundancy Measure, Superpixel.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: 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 post-processing 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 pro posed criterion, gives the highest similarity with the ground-truth segmentations, available in the database. (More)

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Paper citation in several formats:
Murashov, D. (2017). Segmentation Technique based on Information Redundancy Minimization. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP; ISBN 978-989-758-225-7; ISSN 2184-4321, SciTePress, pages 587-594. DOI: 10.5220/0006173005870594

@conference{visapp17,
author={Dmitry Murashov},
title={Segmentation Technique based on Information Redundancy Minimization},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP},
year={2017},
pages={587-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006173005870594},
isbn={978-989-758-225-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP
TI - Segmentation Technique based on Information Redundancy Minimization
SN - 978-989-758-225-7
IS - 2184-4321
AU - Murashov, D.
PY - 2017
SP - 587
EP - 594
DO - 10.5220/0006173005870594
PB - SciTePress

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