The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
Open accessAbstract
In this paper, we propose a novel approach to extract primary object segments in videos in the `object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.
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Cites
GrabCut: Interactive foreground extraction using iterated graph cuts
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Alexe, B., Deselaers, T., & Ferrari, V. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540226 · Cited by 776
Object Segmentation by Long Term Analysis of Point Trajectories
Brox, T., & Malik, J. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_21 · Cited by 744
Class segmentation and object localization with superpixel neighborhoods
Fulkerson, B., Vedaldi, A., & Soatto, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459175 · Cited by 649
Efficient hierarchical graph-based video segmentation
Grundmann, M., Kwatra, V., Han, M., & Essa, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539893 · Cited by 628
Key-segments for video object segmentation
Lee, Y. J., Kim, J., & Grauman, K. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126471 · Cited by 443
Category Independent Object Proposals
Endres, I., & Hoiem, D. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_42 · Cited by 432
Constrained parametric min-cuts for automatic object segmentation
Carreira, J., & Sminchisescu, C. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540063 · Cited by 420
Video SnapCut: robust video object cutout using localized classifiers
Bai, X., Wang, J., Simons, D., & Sapiro, G. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531376 · Cited by 367
Cited by
Jain, S. D., Xiong, B., & Grauman, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.228 · Cited by 361
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00933 · Cited by 303
Learning Motion Patterns in Videos
Tokmakov, P., Alahari, K., & Schmid, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.64 · Cited by 267
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Self-citation
Metadata
- Authors
- Zhang, D., Javed, O., & Shah, M.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 628-635
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2155598147
- Cited by
- 309
- References
- 20
- FWCI
- 27.09
- Type
- conference paper
- Language
- en
- Open access
- green
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