Salient Object Detection via Fusion of Multi-Visual Perception
Source abstract
Abstract
Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method for salient object detection leveraging multi-visual perception, mirroring the human visual system’s rapid identification, and focusing on impressive objects/regions within complex scenes. First, a feature map is derived from the original image. Then, salient object detection results are obtained for each perception feature and combined via a feature fusion strategy to produce a saliency map. Finally, superpixel segmentation is employed for precise salient object extraction, removing interference areas. This multi-feature approach for salient object detection harnesses complementary features to adapt to complex scenarios. Competitive experiments on the MSRA10K and ECSSD datasets place our method in the first tier, achieving 0.1302 MAE and 0.9382 F-measure for the MSRA10K dataset and 0.0783 MAE and and 0.9635 F-measure for the ECSSD dataset, demonstrating superior salient object detection performance in complex natural scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Visual Attention and Saliency Detection · Advanced Image Fusion Techniques · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Key Laboratory of Icing and Anti/De-Icing, China Aerodynamics Research and Development Center, Mianyang 621000, China
Funding
Natural Science Foundation of Sichuan, China · Key Laboratory of Icing and Anti/De-Icing of CARDC
References (48)
Show source-provided references
1. Gao, S., Zhang, W., Wang, Y., Guo, Q., Zhang, C., He, Y., and Zhang, W. (March, January 22). Weakly-supervised salient object detection using point supervision. Proceedings of the AAAI Conference on Artificial Intelligence, Online.
2. Saliency bagging: A novel framework for robust salient object detection10.1007/s00371-019-01750-2 ↗
3. Salient object detection from low contrast images based on local contrast enhancing and non-local feature learning10.1007/s00371-020-01964-9 ↗
4. Salient object detection: A survey10.1007/s41095-019-0149-9 ↗
5. CGAN: Closure-guided attention network for salient object detection10.1007/s00371-021-02222-2 ↗
6. A low computational complexity algorithm for real-time salient object detection10.1007/s00371-022-02513-2 ↗
7. Two-stage salient object detection based on prior distribution learning and saliency consistency optimization10.1007/s00371-022-02692-y ↗
8. A novel multi-graph framework for salient object detection10.1007/s00371-019-01637-2 ↗
9. Salient object detection via hybrid upsampling and hybrid loss computing10.1007/s00371-019-01659-w ↗
10. A brief survey of visual saliency detection10.1007/s11042-020-08849-y ↗
11. Salient object detection: A benchmark10.1109/TIP.2015.2487833 ↗
12. Salient object detection in the deep learning era: An in-depth survey10.1109/TPAMI.2021.3051099 ↗
13. Fully convolutional networks for semantic segmentation10.1109/TPAMI.2016.2572683 ↗
14. Li, G., and Yu, Y. (2015, January 7–12). Visual saliency based on multiscale deep features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
15. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
16. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
17. No-reference image quality assessment in the spatial domain10.1109/TIP.2012.2214050 ↗
18. Global contrast based salient region detection10.1109/TPAMI.2014.2345401 ↗
19. Yan, Q., Xu, L., Shi, J., and Jia, J. (2013, January 23–28). Hierarchical saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.153 ↗
20. A model of saliency-based visual attention for rapid scene analysis10.1109/34.730558 ↗
21. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., and Li, S. (2013, January 23–28). Salient object detection: A discriminative regional feature integration approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.271 ↗
22. Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20–25). Frequency-tuned salient region detection. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPRW.2009.5206596 ↗
23. Yang, C., Zhang, L., Lu, H., Ruan, X., and Yang, M.H. (2013, January 23–28). Saliency detection via graph-based manifold ranking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.407 ↗
24. Jia, Y., and Han, M. (2013, January 23–28). Category-independent object-level saliency detection. Proceedings of the IEEE International Conference on Computer Vision, Portland, OR, USA.10.1109/ICCV.2013.221 ↗
25. Liu, R., Cao, J., Lin, Z., and Shan, S. (2014, January 23–28). Adaptive partial differential equation learning for visual saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.494 ↗
26. Exploiting color name space for salient object detection10.1007/s11042-019-07970-x ↗
27. Salient object detection with high-level prior based on Bayesian fusion10.1049/iet-cvi.2016.0263 ↗
28. Salient object detection via effective background prior and novel graph10.1007/s11042-020-09226-5 ↗
29. Visual saliency detection by integrating spatial position prior of object with background cues10.1016/j.eswa.2020.114219 ↗
30. Changle, Z. (2021). Machine Consciousness, China Machine Press.
31. Jiang, H., Wang, J., Yuan, Z., Liu, T., and Zheng, N. (September, January 29). Automatic salient object segmentation based on context and shape prior. Proceedings of the British Machine Vision Conference, Dundee, UK.
32. He, Y., Xiang, S., Zhou, W., Peng, B., Wang, R., and Li, L. (2021, January 19–22). A Novel Contrast Operator for Robust Object Searching. Proceedings of the 2021 17th International Conference on Computational Intelligence and Security (CIS), Chengdu, China.10.1109/CIS54983.2021.00071 ↗
33. Blind image quality assessment based on multi-scale KLT10.1109/TMM.2020.3001537 ↗
34. Zhu, C., Li, G., Wang, W., and Wang, R. (2017, January 22–29). An innovative salient object detection using center-dark channel prior. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.10.1109/ICCVW.2017.178 ↗
35. Li, J., Zhou, X., Zheng, H., Gao, Q., and Tong, T. (2020, January 13–16). Saliency Detection Based on Dark Channel Prior and Foreground Saliency Probability. Proceedings of the 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC), Fuzhou, China.10.1109/CSRSWTC50769.2020.9372633 ↗
36. Single image haze removal using dark channel prior
37. SLIC superpixels compared to state-of-the-art superpixel methods10.1109/TPAMI.2012.120 ↗
38. Song, C., Wu, J., Deng, H., and Zhu, L. (2020, January 6–8). A salient object detection algorithm based on RGB-D images. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.10.1109/CAC51589.2020.9327554 ↗
39. Hierarchical image saliency detection on extended CSSD10.1109/TPAMI.2015.2465960 ↗
40. Achanta, R., and Süsstrunk, S. (2010, January 26–29). Saliency detection using maximum symmetric surround. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China.10.1109/ICIP.2010.5652636 ↗
41. Visual saliency estimation by nonlinearly integrating features using region covariances10.1167/13.4.11 ↗
42. Graph-regularized saliency detection with convex-hull-based center prior10.1109/LSP.2013.2260737 ↗
43. Margolin, R., Tal, A., and Zelnik-Manor, L. (2013, January 23–28). What makes a patch distinct?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2013.151 ↗
44. Learning discriminative subspaces on random contrasts for image saliency analysis10.1109/TNNLS.2016.2522440 ↗
45. Exploiting color volume and color difference for salient region detection10.1109/TIP.2018.2847422 ↗
46. Fast visual saliency based on multi-scale difference of Gaussians fusion in frequency domain10.1049/iet-ipr.2020.0773 ↗
47. Mignotte, M. (2023). Saliency Map Estimation Using a Pixel-Pairwise-Based Unsupervised Markov Random Field Model. Mathematics, 11.10.3390/math11040986 ↗
48. Multitask learning for image translation and salient object detection from multimodal remote sensing images10.1007/s00371-023-02857-3 ↗