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Learning to Detect A Salient Object
Abstract
We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper.
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Cited by
Saliency filters: Contrast based filtering for salient region detection
Perazzi, F., Krahenbuhl, P., Pritch, Y., & Hornung, A. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247743 · Cited by 2,110
Design and perceptual validation of performance measures for salient object segmentation
Movahedi, V., & Elder, J. H. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543739 · Cited by 469
Bayesian Saliency via Low and Mid Level Cues
Xie, Y., Lu, H., & Yang, M.-H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2216276 · Cited by 367
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping
Wang, W., Shen, J., & Ling, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 348
Revisiting Video Saliency: A Large-Scale Benchmark and a New Model
Wang, W., Shen, J., Guo, F., Cheng, M.-M., & Borji, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00514 · Cited by 268
Metadata
- Authors
- Liu, T., Sun, J., Zheng, N.-N., Tang, X., & Shum, H.-Y.
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2157554677
- Cited by
- 946
- References
- 0
- FWCI
- 30.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, T., Sun, J., Zheng, N.-N., Tang, X., & Shum, H.-Y. (2007). Learning to Detect A Salient Object. 2007 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2007.383047
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