Theoretical-information quality model for image segmentation

被引：4
|
作者：
Murashov, Dmitry [1 ]
机构：
[1] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Vavilov St 40, Moscow 119333, Russia
来源：
3RD INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGY AND NANOTECHNOLOGY (ITNT-2017) | 2017年 / 201卷
基金：
俄罗斯基础研究基金会;
关键词：
Image Segmentation; Segmentation Quality Model; Information Redundancy Measure; Variation of Information;
D O I：
10.1016/j.proeng.2017.09.603
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A problem of image segmentation quality is considered. The problem is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. A segmentation quality model for selecting the best segmentation based on information redundancy measure is proposed. The developed technique was applied to SLIC and graph-cut segmentation algorithms. Computing experiment confirmed that the segmented image corresponding to minimum redundancy measure demonstrates suitable dissimilarity when compared with the original image. The segmented image which was selected using the proposed criterion, gives the highest similarity with the ground-truth segmentations. (C) 2017 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：239 / 248
页数：10
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