 |
Paper accepted at 17th International Conference on Computer Analysis of Images and Patterns (CAIP 2017) |
|---|
This paper is selected as the Sample Paper in CAIP2017
The extended version " A multilayer backpropagation saliency detection algorithm and its applications " has been pubilshed in Multimed Tools Appl 2018. You can download in here.
Saliency detection is an active topic in multimedia field. Several algorithms have been proposed in this field. Most previous works on saliency detection focus on 2D images. However, for some complex situations which contain multiple objects or complex background, they are not robust and their performances are not satisfied. Recently, 3D visual information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The evaluation of the proposed algorithm on two challenging datasets shows that our algorithm outperforms state-of-the-art.
https://chunbiaozhu.github.io/CAIP2017/
You can download our source code in here.
How to use:
1. Add test image to ./center_prior/Image/ and ./Image/, then run center_prior to get the center image in ./center_prior/center_results/.
2. Add depth to ./Depth/, then run OURS1.m show the first layer result in ./OURS1/.
3. Run OURS2.m, then get the second layer result in ./OURS2/.
4. Run OURS.m, then get our final result in ./OURS/.
If you encounter an error, please restart MATLAB and re-run the code as described above.
If you have any question,please email us!
You can download our results on RGBD1_Dataset in here.
You can download our results on RGBD2_Dataset in here.
If you have any question,please email us!
PKU80-Dataset is public.
You can download in here
If you were interested in this work, you may want to also check our posterior work, ICCV2017, which offers a novel idea.
If you were interested in this work, you may want to also check our posterior work, MM2017, which shows a novel application.
This work was supported by the grant of National Natural Science Foundation of China (No.U1611461), the grant of Science and Technology Planning Project of Guangdong Province, China (No.2014B090910001), the grant of Guangdong Province Projects of 2014B010117007 and the grant of Shenzhen Peacock Plan (No.20130408-183003656).
If you have any general doubt about our work or code which may be of interest for other researchers, please use the public issues section on this github repo. Alternatively, drop us an e-mail at mailto:zhuchunbiao@pku.edu.cn.
1. Zhu C., Li G., Guo X., Wang W., Wang R. (2017) A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining. In: Felsberg M., Heyden A., Krüger N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science, vol 10425. Springer, Cham
2. Chunbiao Zhu and Ge Li, A multilayer backpropagation saliency detection algorithm and its applications. Multimed Tools Appl (2018). https://doi.org/10.1007/s11042-018-5780-4
1. @Inbook{Zhu2017,
author="Zhu, Chunbiao
and Li, Ge
and Guo, Xiaoqiang
and Wang, Wenmin
and Wang, Ronggang",
title="A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining",
bookTitle="Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="14--23",
abstract="Saliency detection is an active topic in multimedia field. Several algorithms have been proposed in this field. Most previous works on saliency detection focus on 2D images. However, for some complex situations which contain multiple objects or complex background, they are not robust and their performances are not satisfied. Recently, 3D visual information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from four different layers of images. The evaluation of the proposed algorithm on two challenging datasets shows that our algorithm outperforms state-of-the-art.",
isbn="978-3-319-64698-5",
doi="10.1007/978-3-319-64698-5_2",
url="https://doi.org/10.1007/978-3-319-64698-5_2"
}
2. @Article{Zhu2018,
author="Zhu, Chunbiao
and Li, Ge",
title="A multilayer backpropagation saliency detection algorithm and its applications",
journal="Multimedia Tools and Applications",
year="2018",
month="Mar",
day="07",
abstract="Saliency detection is an active topic in the multimedia field. Most previous works on saliency detection focus on 2D images. However, these methods are not robust against complex scenes which contain multiple objects or complex backgrounds. Recently, depth information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The proposed algorithm shows a good performance and maintains the robustness in complex situations. Experiments' results show that the proposed framework is superior to other existing saliency approaches. Besides, we give two innovative applications by this algorithm, such as scene reconstruction from multiple images and small target object detection in video.",
issn="1573-7721",
doi="10.1007/s11042-018-5780-4",
url="https://doi.org/10.1007/s11042-018-5780-4"
}