IEICE TRANSACTIONS on Information
Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis
Summary :
In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.479-484
- Publication Date
- 2015/02/01
- Publicized
- 2014/11/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8123
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Jungwon KANG
KAIST
Myung Jin CHUNG
KAIST
Keyword
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Jungwon KANG, Myung Jin CHUNG, "Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 479-484, February 2015, doi: 10.1587/transinf.2014EDL8123.
Abstract: In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8123/_p
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@ARTICLE{e98-d_2_479,
author={Jungwon KANG, Myung Jin CHUNG, },
journal={IEICE TRANSACTIONS on Information},
title={Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis},
year={2015},
volume={E98-D},
number={2},
pages={479-484},
abstract={In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2014EDL8123},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 479
EP - 484
AU - Jungwon KANG
AU - Myung Jin CHUNG
PY - 2015
DO - 10.1587/transinf.2014EDL8123
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
AB - In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.
ER -
English
