Video Segmentation with Motion Smoothness

Chung-Lin WEN; Bing-Yu CHEN; Yoichi SATO

doi:10.1587/transinf.E93.D.873

Video Segmentation with Motion Smoothness

Chung-Lin WEN, Bing-Yu CHEN, Yoichi SATO

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In this paper, we present an interactive and intuitive graph-cut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.4 pp.873-881

Publication Date: 2010/04/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.873

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

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Chung-Lin WEN, Bing-Yu CHEN, Yoichi SATO, "Video Segmentation with Motion Smoothness" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 4, pp. 873-881, April 2010, doi: 10.1587/transinf.E93.D.873.
Abstract: In this paper, we present an interactive and intuitive graph-cut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.873/_p

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@ARTICLE{e93-d_4_873,
author={Chung-Lin WEN, Bing-Yu CHEN, Yoichi SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Video Segmentation with Motion Smoothness},
year={2010},
volume={E93-D},
number={4},
pages={873-881},
abstract={In this paper, we present an interactive and intuitive graph-cut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.},
keywords={},
doi={10.1587/transinf.E93.D.873},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Video Segmentation with Motion Smoothness
T2 - IEICE TRANSACTIONS on Information
SP - 873
EP - 881
AU - Chung-Lin WEN
AU - Bing-Yu CHEN
AU - Yoichi SATO
PY - 2010
DO - 10.1587/transinf.E93.D.873
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2010
AB - In this paper, we present an interactive and intuitive graph-cut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.
ER -