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Saliency Detection Inspired by Topological Perception Theory

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Abstract

The topological perception theory claims that visual perception of a scene begins from topological properties and then exploits local details. Inspired by this theory, we defined the topological descriptor and topological complexity, and we observed, based on statistics, that the saliencies of the regions with higher topological complexities are generally higher than those of regions with lower topological complexities. We then introduced the topological complexity as a saliency prior and proposed a novel unsupervised topo-prior-guided saliency detection system (TOPS). This system is framed as a topological saliency prior (topo-prior)-guided two-level local cue processing (i.e., pixel- and regional-level cues) with a multi-scale strategy, which includes three main modules: (1) a basic computational model of the topological perception theory for extracting topological features from images, (2) a topo-prior calculation method based on the topological features, and (3) a global–local saliency combination framework guided by the topo-prior. Extensive experiments on widely used salient object detection (SOD) datasets demonstrate that our system outperforms the unsupervised state-of-the-art algorithms. In addition, the topo-prior proposed in this work can be used to boost supervised methods including the deep-learning-based ones for fixation prediction and SOD tasks.

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Notes

  1. http://www.neuro.uestc.edu.cn/vccl/

  2. http://www.cs.bu.edu/groups/ivc/software/BMS/

  3. https://saliency.tuebingen.ai/

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Acknowledgements

The authors would like to thank Professor Lin Chen for his helpful discussions and suggestions on the modeling of his topological perception theory. This work was supported by the Key Area R&D Program of Guangdong Province (#2018B030338001), the Natural Science Foundations of China (#62076055, #61806041). This work was also supported by the 111 Project (B12027) of China. We also thank LetPub for its linguistic assistance during the preparation of this manuscript.

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  1. MOE Key Laboratory for Neuroinformation, the School of Life Science and Tecnology, University of Electronic Science and Technology of China, 610054, Chengdu, China

    Peng Peng, Kai-Fu Yang, Fu-Ya Luo & Yong-Jie Li

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Peng, P., Yang, KF., Luo, FY. et al. Saliency Detection Inspired by Topological Perception Theory. Int J Comput Vis 129, 2352–2374 (2021). https://doi.org/10.1007/s11263-021-01478-4

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