Abstract
We propose a bootstrap learning algorithm for salient object detection in which both weak and strong models are exploited. First, a weak saliency map is constructed based on image priors to generate training samples for a strong model. Second, a strong classifier based on samples directly from an input image is learned to detect salient pixels. Results from multiscale saliency maps are integrated to further improve the detection performance. Extensive experiments on six benchmark datasets demonstrate that the proposed bootstrap learning algorithm performs favorably against the state-of-the-art saliency detection methods. Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
Original language | English |
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Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
Publisher | IEEE Computer Society |
Pages | 1884-1892 |
Number of pages | 9 |
ISBN (Electronic) | 9781467369640 |
DOIs | |
Publication status | Published - 2015 Oct 14 |
Event | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 2015 Jun 7 → 2015 Jun 12 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 07-12-June-2015 |
ISSN (Print) | 1063-6919 |
Other
Other | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
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Country/Territory | United States |
City | Boston |
Period | 15/6/7 → 15/6/12 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition