DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

Xi Li, Liming Zhao, Lina Wei, Ming Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang

Research output: Contribution to journalArticle

300 Citations (Scopus)

Abstract

A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner. In this paper, we propose a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (whole saliency maps). In principle, the proposed saliency model takes a data-driven strategy for encoding the underlying saliency prior information, and then sets up a multi-task learning scheme for exploring the intrinsic correlations between saliency detection and semantic image segmentation. Through collaborative feature learning from such two correlated tasks, the shared fully convolutional layers produce effective features for object perception. Moreover, it is capable of capturing the semantic information on salient objects across different levels using the fully convolutional layers, which investigate the feature-sharing properties of salient object detection with a great reduction of feature redundancy. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.

Original languageEnglish
Article number7488288
Pages (from-to)3919-3930
Number of pages12
JournalIEEE Transactions on Image Processing
Volume25
Issue number8
DOIs
Publication statusPublished - 2016 Aug

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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