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"GrabCut": interactive foreground extraction using iterated graph cuts

Authors:

Carsten Rother,

Vladimir Kolmogorov,

Andrew BlakeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 23, Issue 3

Pages 309 - 314

https://doi.org/10.1145/1015706.1015720

Published: 01 August 2004 Publication History

Abstract

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.

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Cited By

Nuanmeesri S(2024)Utilization of Multi-Channel Hybrid Deep Neural Networks for Avocado Ripeness ClassificationEngineering, Technology & Applied Science Research10.48084/etasr.765114:4(14862-14867)Online publication date: 2-Aug-2024
https://doi.org/10.48084/etasr.7651
Liu RZhu TWu JLi J(2024)Potato powdery scab segmentation using improved GrabCut algorithmJournal of Agricultural Engineering10.4081/jae.2024.1585Online publication date: 9-May-2024
https://doi.org/10.4081/jae.2024.1585
Xu HZhao XYin QDou JLiu RWang W(2024)Isolating switch state detection system based on depth information guidanceElectronic Research Archive10.3934/era.202404032:2(836-856)Online publication date: 2024
https://doi.org/10.3934/era.2024040
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Index Terms

"GrabCut": interactive foreground extraction using iterated graph cuts
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Computer graphics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts
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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 23, Issue 3

August 2004

684 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1015706

Editor:
John C. Hart

Issue’s Table of Contents

Seminal Graphics Papers: Pushing the Boundaries, Volume 2
August 2023
893 pages
ISBN:9798400708978
DOI:10.1145/3596711
Editor:
Mary C. Whitton
Department of Computer Science, UNC Chapel Hill, USA

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2004

Published in TOG Volume 23, Issue 3

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Cited By

Nuanmeesri S(2024)Utilization of Multi-Channel Hybrid Deep Neural Networks for Avocado Ripeness ClassificationEngineering, Technology & Applied Science Research10.48084/etasr.765114:4(14862-14867)Online publication date: 2-Aug-2024
https://doi.org/10.48084/etasr.7651
Liu RZhu TWu JLi J(2024)Potato powdery scab segmentation using improved GrabCut algorithmJournal of Agricultural Engineering10.4081/jae.2024.1585Online publication date: 9-May-2024
https://doi.org/10.4081/jae.2024.1585
Xu HZhao XYin QDou JLiu RWang W(2024)Isolating switch state detection system based on depth information guidanceElectronic Research Archive10.3934/era.202404032:2(836-856)Online publication date: 2024
https://doi.org/10.3934/era.2024040
Shao YChen YYang PCheng F(2024)EnNet: Enhanced Interactive Information Network with Zero-Order OptimizationSensors10.3390/s2419636124:19(6361)Online publication date: 30-Sep-2024
https://doi.org/10.3390/s24196361
Chen ZZhao RGuo XXie JHan X(2024)Moving Object Detection in Freely Moving Camera via Global Motion Compensation and Local Spatial Information FusionSensors10.3390/s2409285924:9(2859)Online publication date: 30-Apr-2024
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Pang SThio TSiaw FChen MXia Y(2024)Research on Improved Image Segmentation Algorithm Based on GrabCutElectronics10.3390/electronics1320406813:20(4068)Online publication date: 16-Oct-2024
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