# Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion

arXiv: https://arxiv.org/abs/1710.05174 · HTML: https://mlsanity.com/paper/1710.05174/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2017.

## Built on by (most cited first, 28 of 28)

- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [RGB-D Saliency Detection via Cascaded Mutual Information Minimization](https://mlsanity.com/paper/2109.07246/) (2021)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.01971/) (2021)
- [RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2110.14223/) (2021)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2107.01779/) (2021)
- [BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation](https://mlsanity.com/paper/2107.12541/) (2021)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2207.07898/) (2022)
- [Global-and-Local Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2204.08917/) (2022)
- [Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution](https://mlsanity.com/paper/2306.00386/) (2023)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction](https://mlsanity.com/paper/2112.02252/) (2021)
- [DFTR: Depth-supervised Fusion Transformer for Salient Object Detection](https://mlsanity.com/paper/2203.06429/) (2022)
- [Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.03105/) (2022)
- [Transformer-based Network for RGB-D Saliency Detection](https://mlsanity.com/paper/2112.00582/) (2021)
- [Densely Deformable Efficient Salient Object Detection Network](https://mlsanity.com/paper/2102.06407/) (2021)
- [SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification](https://mlsanity.com/paper/2207.04224/) (2022)
- [NAIMA: Semantics Aware RGB Guided Depth Super-Resolution](https://mlsanity.com/paper/2604.04407/) (2026)
- [Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2106.03941/) (2021)
- [Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.06281/) (2021)
- [ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2109.04627/) (2021)
- [Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2201.09574/) (2022)
- [Dynamic Message Propagation Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.09552/) (2022)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection](https://mlsanity.com/paper/2208.03918/) (2022)
- [Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection](https://mlsanity.com/paper/2302.08052/) (2023)
- [Unleashing the Power of Motion and Depth: A Selective Fusion Strategy for RGB-D Video Salient Object Detection](https://mlsanity.com/paper/2507.21857/) (2025)

## Abstract (arXiv)

Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. Firstly, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection. Then, the input image is represented as a graph, and the depth information is introduced into graph construction. After that, a new definition of compactness using color and depth cues is put forward to compute the compactness saliency map. In order to compensate the detection errors of compactness saliency when the salient regions have similar appearances with background, foreground saliency map is calculated based on depth-refined foreground seeds selection mechanism and multiple cues contrast. Finally, these two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance. Experiments on two publicly available stereo datasets demonstrate that the proposed method performs better than other 10 state-of-the-art approaches.