Please use this identifier to cite or link to this item: http://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: 30th IEEE 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
RMID: 0030076454
DOI: 10.1109/CVPR.2017.676
Appears in Collections:Computer Science publications

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