Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/632926
Title: DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Authors: Xi Li;Liming Zhao;Lina Wei;Ming-Hsuan Yang;Fei Wu;Yueting Zhuang;Haibin Ling;Jingdong Wang
subject: datadriven|CNN|multi-task|salient object detection
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/166542
http://localhost/handle/Hannan/632926
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
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Title: DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Authors: Xi Li;Liming Zhao;Lina Wei;Ming-Hsuan Yang;Fei Wu;Yueting Zhuang;Haibin Ling;Jingdong Wang
subject: datadriven|CNN|multi-task|salient object detection
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/166542
http://localhost/handle/Hannan/632926
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7488288.pdf4.15 MBAdobe PDFThumbnail
Preview File
Title: DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Authors: Xi Li;Liming Zhao;Lina Wei;Ming-Hsuan Yang;Fei Wu;Yueting Zhuang;Haibin Ling;Jingdong Wang
subject: datadriven|CNN|multi-task|salient object detection
Year: 2016
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/166542
http://localhost/handle/Hannan/632926
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7488288.pdf4.15 MBAdobe PDFThumbnail
Preview File