Salient Object Detection by Lossless Feature Reflection
Salient Object Detection by Lossless Feature Reflection
Pingping Zhang, Wei Liu, Huchuan Lu, Chunhua Shen
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1149-1155.
https://doi.org/10.24963/ijcai.2018/160
Salient object detection, 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 real-world 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 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 structural loss function to learn 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.
Keywords:
Machine Learning: Classification
Humans and AI: Cognitive Modeling
Machine Learning: Deep Learning
Machine Learning: Structured Prediction