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Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection.
- Source :
- IEEE Transactions on Image Processing; Jan2018, Vol. 27 Issue 1, p121-134, 14p
- Publication Year :
- 2018
-
Abstract
- In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 27
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 125813553
- Full Text :
- https://doi.org/10.1109/TIP.2017.2756825