Skip to main content
SpringerLink
Log in

PS-Net: Progressive Selection Network for Salient Object Detection

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Low-level features contain abundant details and high-level features have rich semantic information. Integrating multi-scale features in an appropriate way is significant for salient object detection. However, direct concatenation or addition taken by most methods ignores the distinctions of contribution among multi-scale features. Besides, most salient object detection models fail to dynamically adjust receptive fields to fit objects of various sizes. To tackle these problems, we propose a Progressive Selection Network (PS-Net). Specifically, PS-Net dynamically extracts high-level features and encourages high-level features to guide low-level features to suppress the background response of the original features. We proposed a salient model PS-Net that selects features progressively at multiply levels. First, we propose a Pyramid Feature Dynamic Extraction module to dynamically select appropriate receptive fields to extract high-level features by Feature Dynamic Extraction modules step by step. Besides, a Self-Interaction Attention module is designed to extract detailed information for low-level features. Finally, we design a Scale Aware Fusion module to fuse these multiple features for adequate exploitation of high-level features to refine low-level features gradually. Compared with 19 start-of-the-art methods on 6 public benchmark datasets, the proposed method achieves remarkable performance in both quantitative and qualitative evaluation. We performed a lot of ablation studies, and more discussions to demonstrate the effectiveness and superiority of our proposed method. In this paper, we propose a PS-Net for effective salient object detection. Extensive experiments on 6 datasets validate that the proposed model outperforms 19 state-of-the-art methods under different evaluation metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Rutishauser U, Walther D, Koch C, Perona P. Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE Computer Society Conf Comp Vis Patt Recogn, 2004. CVPR 2004. 2004;2:II–II. IEEE

  2. Cheng MM, Zhang FL, Mitra NJ, Huang X, Hu SM. Repfinder: finding approximately repeated scene elements for image editing. ACM Transactions on Graphics (TOG). 2010;29(4):1–8.

    Article  Google Scholar 

  3. He J, Feng J, Liu X, Cheng T, Lin TH, Chung H, Chang SF. Mobile product search with bag of hash bits and boundary reranking. In: 2012 IEEE Conf Comp Vis Patt Recogn. 2012:3005–3012. IEEE

  4. Wang W, Shen J, Porikli F. Saliency-aware geodesic video object segmentation. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2015:3395–3402.

  5. Wang W, Shen J, Sun H, Shao L. Video co-saliency guided co-segmentation. IEEE Trans Circuits Syst Video Technol. 2017;28(8):1727–36.

    Article  Google Scholar 

  6. Hong S, You T, Kwak S, Han B. Online tracking by learning discriminative saliency map with convolutional neural network. In: Int Conf Mach Learn. 2015:597–606.

  7. Jiang Z, Davis LS. Submodular salient region detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2013:2043–2050.

  8. Cheng MM, Mitra NJ, Huang X, Torr PH, Hu SM. Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell. 2014;37(3):569–82.

    Article  Google Scholar 

  9. Yang C, Zhang L, Lu H, Ruan X, Yang MH. Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2013:3166–3173.

  10. Luo Z, Mishra A, Achkar A, Eichel J, Li S, Jodoin PM. Non-local deep features for salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2017:6609–6617.

  11. Zhang P, Wang D, Lu H, Wang H, Ruan X. Amulet: Aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE Int Conf Comp Vis. 2017:202–211.

  12. Hou Q, Cheng MM, Hu X, Borji A, Tu Z, Torr PH. Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2017:3203–3212.

  13. Wang L, Lu H, Ruan X, Yang MH. Deep networks for saliency detection via local estimation and global search. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2015:3183–3192.

  14. Wu Z, Su L, Huang Q. Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE/CVF Conf Comp Vis Patt Recogn. 2019:3907–3916.

  15. Liu JJ, Hou Q, Cheng MM, Feng J, Jiang J. A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2019:3917–3926.

  16. Zhao JX, Liu JJ, Fan DP, Cao Y, Yang J, Cheng MM. Egnet: Edge guidance network for salient object detection. In: Proceedings of the IEEE/CVF Int Conf Comp Vis. 2019:8779–8788.

  17. Qin Y, Kamnitsas K, Ancha S, Nanavati J, Cottrell G, Criminisi A, Nori A. Autofocus layer for semantic segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer. 2018:603–611.

  18. Chen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. https://arxiv.org/abs/1706.05587. 2017.

  19. Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2018:7132–7141.

  20. Yu C, Wang J, Peng C, Gao C, Yu G, Sang N. Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2018:1857–1866.

  21. Klein DA, Frintrop S. Center-surround divergence of feature statistics for salient object detection. In: 2011 Int Conf Comp Vis. IEEE. 2011:2214–2219.

  22. Zhao R, Ouyang W, Li H, Wang X. Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2015:1265–1274.

  23. Liu N, Han J. Dhsnet: Deep hierarchical saliency network for salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2016:678–686.

  24. Wang T, Borji A, Zhang L, Zhang P, Lu H. A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE Int Conf Comp Vis. 2017:4019–4028.

  25. Hu X, Zhu L, Qin J, Fu CW, Heng PA. Recurrently aggregating deep features for salient object detection. In: Thirty-second AAAI conference on artificial intelligence. 2018.

  26. Zhang L, Dai J, Lu H, He Y, Wang G. A bi-directional message passing model for salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2018:1741–1750.

  27. Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M. Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conf Comp Vis Patt Recogn. 2019:7479–7489.

  28. Kuen J, Wang Z, Wang G. Recurrent attentional networks for saliency detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2016:3668–3677.

  29. Song H, Wang W, Zhao S, Shen J, Lam KM. Pyramid dilated deeper convlstm for video salient object detection. In: Proceedings of the European conference on computer vision (ECCV). 2018:715–731.

  30. Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X. Residual attention network for image classification. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2017:3156–3164.

  31. Chen L, Zhang H, Xiao J, Nie L, Shao J, Liu W, Chua TS. Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2017:5659–5667.

  32. Xu H, Saenko K. Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In: European Conference on Computer Vision. Springer. 2016:451–466.

  33. Li J, Wei Y, Liang X, Dong J, Xu T, Feng J, Yan S. Attentive contexts for object detection. IEEE Trans Multimedia. 2016;19(5):944–54.

    Article  Google Scholar 

  34. Liu N, Han J, Yang MH. Picanet: Learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2018:3089–3098.

  35. Chen S, Tan X, Wang B, Hu X. Reverse attention for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV). 2018:234–250.

  36. Zhang X, Wang T, Qi J, Lu H, Wang G. Progressive attention guided recurrent network for salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2018:714–722.

  37. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834–48.

    Article  Google Scholar 

  38. Pang Y, Zhao X, Zhang L, Lu H. Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE/CVF Conf Comp Vis Patt Recogn. 2020:9413–9422.

  39. Yan Q, Xu L, Shi J, Jia J. Hierarchical saliency detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2013:1155–1162.

  40. Li G, Yu Y. Visual saliency based on multiscale deep features. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2015:5455–5463.

  41. Li Y, Hou X, Koch C, Rehg JM, Yuille AL. The secrets of salient object segmentation. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2014:280–287.

  42. Wang L, Lu H, Wang Y, Feng M, Wang D, Yin B, Ruan X. Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2017:136–145.

  43. Movahedi V, Elder JH. Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE Computer Society Conf Comp Vis Patt Recogn-Workshops. IEEE. 2010:49–56.

  44. Zhang D, Han J, Zhang Y. Supervision by fusion: Towards unsupervised learning of deep salient object detector. In: Proceedings of the IEEE Int Conf Comp Vis. 2017:4048–4056.

  45. Zhang P, Wang D, Lu H, Wang H, Yin B. Learning uncertain convolutional features for accurate saliency detection. In: Proceedings of the IEEE Int Conf Comp Vis. 2017:212–221.

  46. Chen X, Zheng A, Li J, Lu F. Look, perceive and segment: Finding the salient objects in images via two-stream fixation-semantic cnns. In: Proceedings of the IEEE Int Conf Comp Vis. 2017:1050–1058.

  47. Li X, Yang F, Cheng H, Liu W, Shen D. Contour knowledge transfer for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV). 2018:355–370.

  48. Zeng Y, Zhuge Y, Lu H, Zhang L, Qian M, Yu Y. Multi-source weak supervision for saliency detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2019:6074–6083.

  49. Zhang L, Zhang J, Lin Z, Lu H, He Y. Capsal: Leveraging captioning to boost semantics for salient object detection. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2019:6024–6033.

  50. Zeng Y, Zhang P, Zhang J, Lin Z, Lu H. Towards high-resolution salient object detection. In: Proceedings of the IEEE Int Conf Comp Vis. 2019:7234–7243.

  51. Xu Y, Xu D, Hong X, Ouyang W, Ji R, Xu M, Zhao G. Structured modeling of joint deep feature and prediction refinement for salient object detection. In: Proceedings of the IEEE Int Conf Comp Vis. 2019:3789–3798.

  52. Wang W, Zhao S, Shen J, Hoi SC, Borji A. Salient object detection with pyramid attention and salient edges. In: Proceedings of the IEEE Conf Comp Vis Patt Recogn. 2019:1448–1457.

  53. Gao SH, Tan YQ, Cheng MM, Lu C, Chen Y, Yan S. Highly efficient salient object detection with 100k parameters. 2020. https://arxiv.org/abs/2003.05643.

  54. Zhang J, Yu X, Li A, Song P, Liu B, Dai Y. Weakly-supervised salient object detection via scribble annotations. In: Proceedings of the IEEE/CVF Conf Comp Vis Patt Recogn. 2020:12546–12555.

  55. Zhang J, Xie J, Barnes N. Learning noise-aware encoder-decoder from noisy labels by alternating back-propagation for saliency detection. 2020. https://arxiv.org/abs/2007.12211/.

  56. Zhao X, Pang Y, Zhang L, Lu H, Zhang L. Suppress and balance: A simple gated network for salient object detection. 2020. https://arxiv.org/abs/2007.08074.

Download references

Acknowledgements

The authors wish to acknowledge the support for the research work from the National Natural Science Foundation of China under grant Nos.[61772360] ,[61876125] and [62076180].

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Jianyi Ren & Zheng Wang

  2. Tianjin Key Lab of Machine Learning, Tianjin, China

    Jianyi Ren & Zheng Wang

  3. National Subsea Centre, School of Computing, Robert Gordon University, Aberdeen, UK

    Jinchang Ren

Authors
  1. Jianyi Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinchang Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng Wang.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, J., Wang, Z. & Ren, J. PS-Net: Progressive Selection Network for Salient Object Detection. Cogn Comput 14, 794–804 (2022). https://doi.org/10.1007/s12559-021-09952-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09952-4

Keywords

Search

Navigation