PDNet: Prior-Model Guided Depth-Enhanced Network for Salient Object Detection

Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture—PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGB-D values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches.

[1]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[2]  Huchuan Lu,et al.  Deep networks for saliency detection via local estimation and global search , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Gayoung Lee,et al.  Deep Saliency with Encoded Low Level Distance Map and High Level Features , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Wenhao Zhang,et al.  Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection , 2018, ACM Trans. Intell. Syst. Technol..

[5]  Tongwei Ren,et al.  Salient object detection for RGB-D image via saliency evolution , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[6]  Ge Li,et al.  An Innovative Saliency Guided ROI Selection Model for Panoramic Images Compression , 2018, 2018 Data Compression Conference.

[7]  Jiandong Tian,et al.  RGBD Salient Object Detection via Deep Fusion , 2016, IEEE Transactions on Image Processing.

[8]  Li Xu,et al.  Hierarchical Image Saliency Detection on Extended CSSD , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Haibin Ling,et al.  Saliency Detection on Light Field , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ronggang Wang,et al.  An Innovative Salient Object Detection Using Center-Dark Channel Prior , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[11]  Xiaogang Wang,et al.  Saliency detection by multi-context deep learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Ronggang Wang,et al.  A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining , 2017, CAIP.

[13]  Rongrong Ji,et al.  RGBD Salient Object Detection: A Benchmark and Algorithms , 2014, ECCV.

[14]  Ge Li,et al.  A multilayer backpropagation saliency detection algorithm and its applications , 2018, Multimedia Tools and Applications.

[15]  Ge Li,et al.  A Three-Pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[16]  Junwei Han,et al.  CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion. , 2018, IEEE transactions on cybernetics.

[17]  Huchuan Lu,et al.  Inner and Inter Label Propagation: Salient Object Detection in the Wild , 2015, IEEE Transactions on Image Processing.

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Ge Li,et al.  Towards Automatic Wild Animal Detection in Low Quality Camera-Trap Images Using Two-Channeled Perceiving Residual Pyramid Networks , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[20]  Xiaochun Cao,et al.  Depth Enhanced Saliency Detection Method , 2014, ICIMCS '14.

[21]  Ran Ju,et al.  Depth saliency based on anisotropic center-surround difference , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[22]  Huchuan Lu,et al.  Saliency detection via Cellular Automata , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Qingming Huang,et al.  Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion , 2016, IEEE Signal Processing Letters.

[24]  Ali Borji,et al.  Salient object detection: A survey , 2014, Computational Visual Media.

[25]  Huchuan Lu,et al.  CNN for saliency detection with low-level feature integration , 2017, Neurocomputing.