TY - GEN
T1 - Salient object detection via bootstrap learning
AU - Tong, Na
AU - Lu, Huchuan
AU - Ruan, Xiang
AU - Yang, Ming Hsuan
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/10/14
Y1 - 2015/10/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84959240205&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959240205&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2015.7298798
DO - 10.1109/CVPR.2015.7298798
M3 - Conference contribution
AN - SCOPUS:84959240205
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1884
EP - 1892
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
T2 - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Y2 - 7 June 2015 through 12 June 2015
ER -