The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Deep networks for saliency detection via local estimation and global search
Open accessAbstract
This paper presents a saliency detection algorithm by integrating both local estimation and global search. In the local estimation stage, we detect local saliency by using a deep neural network (DNN-L) which learns local patch features to determine the saliency value of each pixel. The estimated local saliency maps are further refined by exploring the high level object concepts. In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions. Another deep neural network (DNN-G) is trained to predict the saliency score of each object region based on the global features. The final saliency map is generated by a weighted sum of salient object regions. Our method presents two interesting insights. First, local features learned by a supervised scheme can effectively capture local contrast, texture and shape information for saliency detection. Second, the complex relationship between different global saliency cues can be captured by deep networks and exploited principally rather than heuristically. Quantitative and qualitative experiments on several benchmark data sets demonstrate that our algorithm performs favorably against the state-of-the-art methods.
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Cites
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Cited by
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
EGNet: Edge Guidance Network for Salient Object Detection
Zhao, J., Liu, J.-J., Fan, D.-P., Cao, Y., Yang, J., & Cheng, M.-M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00887 · Cited by 1,202
Visual Tracking with Fully Convolutional Networks
Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.357 · Cited by 983
Self-citationDHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Liu, N., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.80 · Cited by 833
Metadata
- Authors
- Wang, L., Lu, H., Ruan, X., & Yang, M.-H.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3183-3192
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1947031653
- Cited by
- 676
- References
- 40
- FWCI
- 29.93
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, L., Lu, H., Ruan, X., & Yang, M.-H. (2015). Deep networks for saliency detection via local estimation and global search. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298938
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