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Proceedings Paper

Video co-saliency detection
Author(s): Yufeng Xie; Linwei Ye; Zhi Liu; Xuemei Zou
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Paper Abstract

In this paper, a novel co-saliency model is proposed with the aim to detect co-salient objects in multiple videos. On the basis of superpixel segmentation results, we fuse the temporal saliency and spatial saliency with a superpixel-level object prior to generate the intra saliency map for each video frame. Then the video-level global object/background histogram is calculated for each video based on the adaptive thresholding results of intra saliency maps, and the seed saliency maps are generated by using similarity measures between superpixels and the global object/background histogram. Finally, the co-saliency maps are generated by the recovery process from the seed saliency measures to all regions in each video frame. Experimental results on a public video dataset show that the proposed video co-saliency model consistently outperforms the state-of-the-art video saliency model and image co-saliency models.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335G (29 August 2016); doi: 10.1117/12.2245113
Show Author Affiliations
Yufeng Xie, Shanghai Univ. (China)
Linwei Ye, Shanghai Univ. (China)
Zhi Liu, Shanghai Univ. (China)
Xuemei Zou, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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