Salient Object Detection via Bootstrap Learning
- Title
- Salient Object Detection via Bootstrap Learning
- Author
- Yang, Ming-hsuan
- Keywords
- Training; Kernel; Computational modeling; Feature extraction; Object detection; Boosting; Support vector machines
- Issue Date
- 2015-06
- Publisher
- IEEE
- Citation
- Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 1884-1892
- 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.
- URI
- http://ieeexplore.ieee.org/document/7298798/?isnumber=7298593&arnumber=7298798http://hdl.handle.net/20.500.11754/25603
- ISBN
- 978-1-4673-6964-0
- ISSN
- 1063-6919; 1063-6919
- DOI
- 10.1109/CVPR.2015.7298798
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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