CAIP2017 - 17th international Conference on Computer Analysis of Images and Patterns
A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining
Chunbiao Zhu1, Ge Li1*, Xiaoqiang Guo2, Ronggang Wang1, Wenmin Wang1
1School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China 2Academy of Broadcasting Science, SAPPRFT Beijing, China
Fig.1 Our Framework. Fig.2 Visual Process of Our Framework.
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 three different layers of images. The evaluation of the proposed algorithm on two challenging datasets shows that our algorithm outperforms state-of-the-art.
Experimental Results
Fig.3 Left:PR curve of different methods on RGBD1* dataset. Right:PR curve of different methods on RGBD2* dataset. Fig.4 Left:ROC curve of different methods on RGBD1* dataset. Right:ROC curve of different methods on RGBD2* dataset. Fig.5 Visual comparison of saliency maps on two datasets.
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Acknowledgements
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).
