Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking

By Cheng Deng, Xu Yang, Feiping Nie, Dapeng Tao. 2020

54 citations recorded by the source corpus.

As an important task in the process of image understanding and analysis, saliency detection has recently received increasing attention. In this paper, we propose an efficient multiple self-weighted graph-based manifold ranking method to construct salient maps. First, we extract several different views of features from superpixels, and generate original salient regions as foreground and background cues using boundary information via multiple graph-based manifold ranking. Furthermore, a set of hyperparameters is learned to distinguish the importance between different graphs, which can be viewed as an adaptive weighting of each graph, and then a centroid graph is generated by using these self-weighted multiple graphs. An iterative algorithm is proposed to simultaneously optimize the hyperparameters as well as the centroid graph connection. Thus, an ideal centroid graph can be obtained, offering a more clear profile of the separated structure. Finally, the saliency maps can be produced with an approximate binary image from the manifold ranking. Extensive experiments have demonstrated our method consistently achieves superior detection performance than several state-of-the-arts.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Cheng Deng
2. Tongliang Liu
3. Yanan Gu
4. Kun-Juan Wei
5. Xu Yang
6. Dacheng Tao
7. Luc Van Gool
8. Yi Yang

In Sensory Systems

1. Cheng Deng
2. Xu Yang

Citations in the corpus

• Class-Incremental Instance Segmentation via Multi-Teacher Networks (2021, background): “Due to the development of deep learning, like other visual tasks (Yang et al. 2018, 2020a; Dang et al. 2020; Yang et al. 2020b; Deng et al. 2019), instance segmentation meth-ods have achieved amazing results.”
• Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation (2020, result): “The deep method can learn the nonlinear representation of different domains and obtain better results compared with the methods based on shallow features [15].”