Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential
Aiming at the problems of poor quality and low time efficiency of video object segmentation in complex scenes, this paper proposes a novel bilateral video object segmentation using dynamic appearance modeling and higher-order potential, which is formulated the video object segmentation problem as bi...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-12-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2489.shtml |
| _version_ | 1818689828156342272 |
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| author | TIAN Ying, GUI Yan, XIONG Daming |
| author_facet | TIAN Ying, GUI Yan, XIONG Daming |
| author_sort | TIAN Ying, GUI Yan, XIONG Daming |
| collection | DOAJ |
| description | Aiming at the problems of poor quality and low time efficiency of video object segmentation in complex scenes, this paper proposes a novel bilateral video object segmentation using dynamic appearance modeling and higher-order potential, which is formulated the video object segmentation problem as binary labeling of Markov random field (MRF) based on bilateral grid cells. Firstly, this paper resamples each pixel in the video sequence with labeled keyframes into a higher-dimensional bilateral grid, which greatly reduces the video data to be processed. Secondly, this paper constructs a graph-cut optimization model using non-empty grid cells as the nodes of graph. The key is to construct the dynamic appearance model with confidence measurements, and to introduce a robust higher-order potential into the energy function. Finally, this paper uses the max-flow/min-cut algorithm to solve the global optimization problem, and binary label assignment of each pixel is achieved to obtain the high-quality video object segmentation. The experimental results on DAVIS 2016 and SegTrack v2 datasets show that with less user interaction, this method can not only obtain high-quality video object segmentation results for videos with complex scenes, but also significantly improve the time efficiency of video object segmentation. |
| first_indexed | 2024-12-17T12:16:18Z |
| format | Article |
| id | doaj.art-9a4dc6a1867e4fbfa19437f12322ce06 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-17T12:16:18Z |
| publishDate | 2020-12-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-9a4dc6a1867e4fbfa19437f12322ce062022-12-21T21:49:10ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-12-0114122108212110.3778/j.issn.1673-9418.1911014Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order PotentialTIAN Ying, GUI Yan, XIONG Daming01. School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China 2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science & Technology, Changsha 410114, ChinaAiming at the problems of poor quality and low time efficiency of video object segmentation in complex scenes, this paper proposes a novel bilateral video object segmentation using dynamic appearance modeling and higher-order potential, which is formulated the video object segmentation problem as binary labeling of Markov random field (MRF) based on bilateral grid cells. Firstly, this paper resamples each pixel in the video sequence with labeled keyframes into a higher-dimensional bilateral grid, which greatly reduces the video data to be processed. Secondly, this paper constructs a graph-cut optimization model using non-empty grid cells as the nodes of graph. The key is to construct the dynamic appearance model with confidence measurements, and to introduce a robust higher-order potential into the energy function. Finally, this paper uses the max-flow/min-cut algorithm to solve the global optimization problem, and binary label assignment of each pixel is achieved to obtain the high-quality video object segmentation. The experimental results on DAVIS 2016 and SegTrack v2 datasets show that with less user interaction, this method can not only obtain high-quality video object segmentation results for videos with complex scenes, but also significantly improve the time efficiency of video object segmentation.http://fcst.ceaj.org/CN/abstract/abstract2489.shtmlvideo object segmentationbilateral spacebilateral gridconfidence-based dynamic appearance modelhigher-order energy potential |
| spellingShingle | TIAN Ying, GUI Yan, XIONG Daming Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential Jisuanji kexue yu tansuo video object segmentation bilateral space bilateral grid confidence-based dynamic appearance model higher-order energy potential |
| title | Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential |
| title_full | Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential |
| title_fullStr | Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential |
| title_full_unstemmed | Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential |
| title_short | Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential |
| title_sort | bilateral video object segmentation using dynamic appearance modeling and higher order potential |
| topic | video object segmentation bilateral space bilateral grid confidence-based dynamic appearance model higher-order energy potential |
| url | http://fcst.ceaj.org/CN/abstract/abstract2489.shtml |
| work_keys_str_mv | AT tianyingguiyanxiongdaming bilateralvideoobjectsegmentationusingdynamicappearancemodelingandhigherorderpotential |