PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection

	Paper accepted at IEEE International Conference on Multimedia and Expo (ICME) 2019 (Oral, Top 15%)

Abstract

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 RGBD 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-theart approaches.

Framework

Lost Dataset (NLPR1000 & NJU2000)

You can download in here and here

Code

Source code is available! You can download in here

If you use this code.

Please cite as:

PDNet: Prior-Model Guided Depth-enhanced Network for Salient Object Detection.
Chunbiao Zhu, Xing Cai, Kan Huang, Thomas.H Li and Ge Li
Arxiv 2018.

@INPROCEEDINGS{PDNet2018,
author=
"Zhu, Chunbiao
and Cai, Xing
and Kan, Huang
and Thomas.H, Li
and Ge, Li",
booktitle={Arxiv},
title={PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection},
year={2018},
}

SSD Dataset

The dataset proposed in our paper is one of our contributions to the research community, which public at ICCVW 2017. This dataset is collected for saliency evaluation on stereo images, and contain a variety of challenging cases.

You can download in here

If you use this dataset.

Please cite as:

G. Li and C. Zhu, “A three-pathway psychobiological framework of salient object detection using stereoscopic technology,” in 2017 ICCVW, Oct 2017, pp. 3008–3014.

Five RGB-D based Dataset

All the datasets we used, you can download in the following link:

Link：https://pan.baidu.com/s/1fIL4T0ZF2V1RAikr0mmpmg pwd：4bwc

benchmark

http://dpfan.net/d3netbenchmark/