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https://hdl.handle.net/2440/110400
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Type: | Conference paper |
Title: | Video Segmentation via Multiple Granularity Analysis |
Author: | Yang, R. Ni, B. Ma, C. Xu, Y. Yang, X. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017, vol.2017-January, pp.6383-6392 |
Publisher: | IEEE |
Issue Date: | 2017 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781538604571 |
ISSN: | 1063-6919 |
Conference Name: | 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (21 Jul 2017 - 26 Jul 2017 : Honolulu) |
Statement of Responsibility: | Rui Yang, Bingbing Ni, Chao Ma, YiXu, Xiaokang Yang |
Abstract: | We introduce a Multiple Granularity Analysis framework for video segmentation in a coarse-to-fine manner. We cast video segmentation as a spatio-temporal superpixel labeling problem. Benefited from the bounding volume provided by off-the-shelf object trackers, we estimate the foreground/ background super-pixel labeling using the spatiotemporal multiple instance learning algorithm to obtain coarse foreground/background separation within the volume. We further refine the segmentation mask in the pixel level using the graph-cut model. Extensive experiments on benchmark video datasets demonstrate the superior performance of the proposed video segmentation algorithm. |
Rights: | © 2017 IEEE |
DOI: | 10.1109/CVPR.2017.676 |
Published version: | http://dx.doi.org/10.1109/cvpr.2017.676 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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