Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/121947
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Type: Journal article
Title: Salient object detection with lossless feature reflection and weighted structural loss
Author: Zhang, P.
Liu, W.
Lu, H.
Shen, C.
Citation: IEEE Transactions on Image Processing, 2019; 28(6):3048-3060
Publisher: IEEE
Issue Date: 2019
ISSN: 1057-7149
1941-0042
Statement of
Responsibility: 
Pingping Zhang, Wei Liu, Huchuan Lu, and Chunhua Shen
Abstract: Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various realworld applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin.
Keywords: Salient object detection; image intrinsic reflection; fully convolutional network; structural feature learning; spatially consistent saliency
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
DOI: 10.1109/TIP.2019.2893535
Appears in Collections:Aurora harvest 4
Computer Science publications

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