JOURNAL ARTICLE

Interactive Foreground/Background Segmentation Based on Graph Cut

Abstract

This paper addresses the problem of natural image segmentation. We develop the interactive image segmentation system and construct two-scale graphs, including region-based graph and pixel-level graph. Building region-level graph is to partition the image into several constituent components. Graph cut is used to extract the foreground object from the image. New data cost functions are defined in the graph cut framework. Under some circumstances, boundary edit based on a pixel-level graph is performed in order to get accurate foreground edges. Experimental results prove that new cost functions are valid and satisfying segmentation results can be obtained by limited user efforts.

Keywords:
Cut Computer science Image segmentation Segmentation Pixel Graph Artificial intelligence Minimum spanning tree-based segmentation Connected-component labeling Segmentation-based object categorization Computer vision Range segmentation Scale-space segmentation Graph partition Pattern recognition (psychology) Theoretical computer science

Metrics

9
Cited By
0.29
FWCI (Field Weighted Citation Impact)
16
Refs
0.68
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

Related Documents

JOURNAL ARTICLE

Unified graph-based method for instance separation from foreground-background segmentation

Milica SpascIgor MihajlovcNikola SpascDragan Jankovc

Journal:   2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) Year: 2022 Vol: 11 Pages: 1-4
JOURNAL ARTICLE

Interactive foreground segmentation method using mean shift and graph cuts

Yuan TianTao GuanCheng WangLijun LiWei Liu

Journal:   Sensor Review Year: 2009 Vol: 29 (2)Pages: 157-162
JOURNAL ARTICLE

Unsupervised foreground segmentation using background elimination and graph cut techniques

K.T. ParkJu Hyoung LeeYecheol Moon

Journal:   Electronics Letters Year: 2009 Vol: 45 (20)Pages: 1025-1027
© 2026 ScienceGate Book Chapters — All rights reserved.