1,055
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Saliency Detection Using a Bio-inspired Spiking Neural Network Driven by Local and Global Saliency

, , , &
Article: 2094408 | Received 01 Apr 2022, Accepted 21 Jun 2022, Published online: 11 Jul 2022

References

  • Achanta, R., S. Hemami, F. Estrada, and S. Susstrunk (2009). Frequency-tuned salient region detection. In 2009 ieee conference on computer vision and pattern recognition, Miami, Florida (pp. 1597–2928).
  • Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. 2012. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11):2274–82. doi:10.1109/TPAMI.2012.120.
  • Alexe, B., T. Deselaers, and V. Ferrari (2010). What is an object? In 2010 ieee computer society conference on computer vision and pattern recognition, San Francisco, CA (pp. 73–80).
  • Alexe, B., T. Deselaers, and V. Ferrari. 2012. Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11):2189–202. doi:10.1109/TPAMI.2012.28.
  • Ban, Z., J. Liu, and L. Cao. 2018. Superpixel segmentation using Gaussian mixture model. IEEE Transactions on Image Processing 27 (8):4105–17. doi:10.1109/TIP.2018.2836306.
  • Becker, C., R. Rigamonti, V. Lepetit, and P. Fua (2013, September). Supervised feature learning for curvilinear structure segmentation. In International conference on medical image computing and computer-assisted intervention, Nagoya, Japan (pp. 526–33).
  • Borji, A., M.-M. Cheng, H. Jiang, and J. Li. 2015. Salient object detection: A benchmark. IEEE Transactions on Image Processing 24 (12):5706–22. doi:10.1109/TIP.2015.2487833.
  • Borji, A., M.-M. Cheng, Q. Hou, H. Jiang, and J. Li. 2019. Salient object detection: A survey. Computational Visual Media 5 (2):117–50. doi:10.1007/s41095-019-0149-9.
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Chai, Y., H. Li, and J. Qu. 2010. Image fusion scheme using a novel dual-channel pcnn in lifting stationary wavelet domain. Optics Communications 283 (19):3591–602. doi:10.1016/j.optcom.2010.04.100.
  • Cong, R., J. Lei, H. Fu, M.-M. Cheng, W. Lin, and Q. Huang. 2018. Review of visual saliency detection with comprehensive information. IEEE Transactions on Circuits and Systems for Video Technology 29 (10):2941–59. doi:10.1109/TCSVT.2018.2870832.
  • Davis, J., and M. Goadrich (2006). The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on machine learning, Pittsburgh Pennsylvania, USA (pp. 233–40).
  • Duan, Q., T. Akram, P. Duan, and X. Wang. 2016. Visual saliency detection using information contents weighting. Optik 127 (19):7418–30. doi:10.1016/j.ijleo.2016.05.027.
  • Fu, K., C. Gong, I. Y.-H. Gu, and J. Yang. 2015. Normalized cut-based saliency detection by adaptive multi-level region merging. IEEE Transactions on Image Processing 24 (12):5671–83. doi:10.1109/TIP.2015.2485782.
  • Goferman, S., L. Zelnik-Manor, and A. Tal. 2011. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (10):1915–26. doi:10.1109/TPAMI.2011.272.
  • Guo, Y., Z. Yang, Y. Ma, J. Lian, and L. Zhu, et al. 2018. Saliency motivated improved simplified pcnn model for object segmentation. Neurocomputing 275:2179–90. doi:10.1016/j.neucom.2017.10.057.
  • Islam, M. A., M. Kalash, and N. D. Bruce (2018). Revisiting salient object detection: Simultaneous detection, ranking, and subitizing of multiple salient objects. In Proceedings of the ieee conference on computer vision and pattern recognition, Salt Lake City, UT, USA (pp. 7142–50).
  • Ji, Y., H. Zhang, Z. Zhang, and M. Liu. 2021. Cnn-based encoder-decoder networks for salient object detection: A comprehensive review and recent advances. Information Sciences 546:835–57. doi:10.1016/j.ins.2020.09.003.
  • Jiang, B., L. Zhang, H. Lu, C. Yang, and M.-H. Yang (2013). Saliency detection via absorbing markov chain. In Proceedings of the ieee international conference on computer vision, Sydney, Australia (pp. 1665–72).
  • Lei, J., B. Wang, Y. Fang, W. Lin, P. Le Callet, N. Ling, and C. Hou. 2016. A universal framework for salient object detection. IEEE Transactions on Multimedia 18 (9):1783–95. doi:10.1109/TMM.2016.2592325.
  • Li, G., and Y. Yu (2015). Visual saliency based on multiscale deep features. In Proceedings of the ieee conference on computer vision and pattern recognition, Boston, MA, USA (pp. 5455–63).
  • Li, C., Y. Yuan, W. Cai, Y. Xia, and D. Dagan Feng (2015a). Robust saliency detection via regularized random walks ranking. In Proceedings of the ieee conference on computer vision and pattern recognition, Boston, MA, USA (pp. 2710–17).
  • Li, H., H. Lu, Z. Lin, X. Shen, and B. Price. 2015b. Inner and inter label propagation: Salient object detection in the wild. IEEE Transactions on Image Processing 24 (10):3176–86. doi:10.1109/TIP.2015.2440174.
  • Liu, T., Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum. 2010. Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (2):353–67.
  • Liu, G.-H., and J.-Y. Yang. 2019. Exploiting color volume and color difference for salient region detection. IEEE Transactions on Image Processing 28 (1):6–16. doi:10.1109/TIP.2018.2847422.
  • Lu, Y., K. Zhou, X. Wu, and P. Gong. 2019. A novel multi-graph framework for salient object detection. The Visual Computer 35 (11):1683–99. doi:10.1007/s00371-019-01637-2.
  • Movahedi, V., and J. H. Elder (2010). Design and perceptual validation of performance measures for salient object segmentation. In 2010 ieee computer society conference on computer vision and pattern recognition-workshop, San Francisco, CAs (pp. 49–56).
  • Mu, X., H. Qi, and X. Li. 2020. Automatic segmentation of images with superpixel similarity combined with deep learning. Circuits, Systems, and Signal Processing 39 (2):884–99. doi:10.1007/s00034-019-01249-0.
  • Pang, Y., X. Yu, Y. Wu, C. Wu, and Y. Jiang. 2020a. Bagging-based saliency distribution learning for visual saliency detection. Signal Processing: Image Communication 87:115928.
  • Pang, Y., X. Yu, Y. Wu, C. Wu, and Y. Jiang. 2020b. Bagging-based saliency distribution learning for visual saliency detection. Signal Processing: Image Communication 87:115928.
  • Peng, H., B. Li, H. Ling, W. Hu, W. Xiong, and S. J. Maybank. 2016. Salient object detection via structured matrix decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4):818–32. doi:10.1109/TPAMI.2016.2562626.
  • Qi, W., -M.-M. Cheng, A. Borji, H. Lu, and L.-F. Bai. 2015. Saliencyrank: Two-stage manifold ranking for salient object detection. Computational Visual Media 1 (4):309–20. doi:10.1007/s41095-015-0028-y.
  • Rahtu, E., J. Kannala, M. Salo, and J. Heikkilä (2010). Segmenting salient objects from images and videos. In European conference on computer vision, Heraklion, Crete, Greece (pp. 366–79).
  • Shariatmadar, Z. S., and K. Faez. 2019. Visual saliency detection via integrating bottom-up and top-down information. Optik 178:1195–207. doi:10.1016/j.ijleo.2018.10.096.
  • Shi, J., Q. Yan, L. Xu, and J. Jia. 2015. Hierarchical image saliency detection on extended cssd. IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (4):717–29. doi:10.1109/TPAMI.2015.2465960.
  • Sun, X., X. Zhang, C. Xu, M. Xiao, and Y. Tang. 2022. Tensorial multiview representation for saliency detection via nonconvex approach. IEEE Transactions on Cybernetics 1–14. doi:10.1109/TCYB.2021.3139037.
  • Tong, N., H. Lu, X. Ruan, and M.-H. Yang (2015a). Salient object detection via bootstrap learning. In Proceedings of the ieee conference on computer vision and pattern recognition, Boston, MA, USA (pp. 1884–92).
  • Tong, N., H. Lu, Y. Zhang, and X. Ruan. 2015b. Salient object detection via global and local cues. Pattern Recognition 48 (10):3258–67. doi:10.1016/j.patcog.2014.12.005.
  • Wang, T., L. Zhang, H. Lu, C. Sun, and J. Qi (2016a). Kernelized subspace ranking for saliency detection. In European conference on computer vision, Amsterdam, The Netherlands (pp. 450–66).
  • Wang, Z., S. Wang, Y. Zhu, and Y. Ma. 2016b. Review of image fusion based on pulse-coupled neural network. Archives of Computational Methods in Engineering 23 (4):659–71. doi:10.1007/s11831-015-9154-z.
  • Wang, M., and X. Shang. 2020. An improved simplified pcnn model for salient region detection. The Visual Computer 38 1–13.
  • Wang, H., C. Zhu, J. Shen, Z. Zhang, and X. Shi. 2021. Salient object detection by robust foreground and background seed selection. Computers & Electrical Engineering 90:106993. doi:10.1016/j.compeleceng.2021.106993.
  • Wang, F., and G. Peng. 2021a. Saliency detection based on color descriptor and high-level prior. Machine Vision and Applications 32 (6):1–12. doi:10.1007/s00138-021-01250-1.
  • Wang, F., and G. Peng. 2021b. Saliency detection based on color descriptor and high-level prior. Machine Vision and Applications 32 1–12.
  • Wu, Z., L. Su, and Q. Huang (2019). Stacked cross refinement network for edge-aware salient object detection. In Proceedings of the ieee/cvf international conference on computer vision, Seoul, Korea (South) (pp. 7264–73).
  • Xu, M., B. Liu, P. Fu, J. Li, and Y. H. Hu. 2019. Video saliency detection via graph clustering with motion energy and spatiotemporal objectness. IEEE Transactions on Multimedia 21 (11):2790–805. doi:10.1109/TMM.2019.2914889.
  • Xu, M., B. Liu, P. Fu, J. Li, Y. H. Hu, and S. Feng. July 2020. Video salient object detection via robust seeds extraction and multi-graphs manifold propagation. IEEE Transactions on Circuits and Systems for Video Technology 30(7):2191–206.
  • Yang, C., L. Zhang, and H. Lu. 2013. Graph-regularized saliency detection with convex-hull- based center prior. IEEE Signal Processing Letters 20 (7):637–40. doi:10.1109/LSP.2013.2260737.
  • Yang, C., L. Zhang, H. Lu, X. Ruan, and M.-H. Yang (2013). Saliency detection via graph- based manifold ranking. In Proceedings of the ieee conference on computer vision and pattern recognition, Portland, OR, USA (pp. 3166–73).
  • Yang, B., and S. Li. 2014. Visual attention guided image fusion with sparse representation. Optik 125 (17):4881–88. doi:10.1016/j.ijleo.2014.04.036.
  • Yuan, Y., C. Li, J. Kim, W. Cai, and D. D. Feng. 2018. Reversion correction and regularized random walk ranking for saliency detection. IEEE Transactions on Image Processing 27 (3):1311–22. doi:10.1109/TIP.2017.2762422.
  • Zeng, Y., P. Zhang, J. Zhang, Z. Lin, and H. Lu (2019a). Towards high-resolution salient object detection. In Proceedings of the ieee/cvf international conference on computer vision, Seoul, Korea (South) (pp. 7234–43).
  • Zeng, Y., Y. Zhuge, H. Lu, and L. Zhang (2019b). Joint learning of saliency detection and weakly supervised semantic segmentation. In Proceedings of the ieee/cvf international conference on computer vision, Seoul, Korea (South) (pp. 7223–33).
  • Zhang, -Y.-Y., Z.-P. Wang, and X.-D. Lv. 2016. Saliency detection via sparse reconstruction errors of covariance descriptors on riemannian manifolds. Circuits, Systems, and Signal Processing 35 (12):4372–89. doi:10.1007/s00034-016-0267-x.
  • Zhang, L., J. Ai, B. Jiang, H. Lu, and X. Li. 2017. Saliency detection via absorbing markov chain with learnt transition probability. IEEE Transactions on Image Processing 27 (2):987–98. doi:10.1109/TIP.2017.2766787.
  • Zhang, M., Y. Pang, Y. Wu, Y. Du, H. Sun, and K. Zhang. 2018a. Saliency detection via local structure propagation. Journal of Visual Communication and Image Representation 52:131–42. doi:10.1016/j.jvcir.2018.01.004.
  • Zhang, Q., J. Lin, W. Li, Y. Shi, and G. Cao. 2018b. Salient object detection via compactness and objectness cues. The Visual Computer 34 (4):473–89. doi:10.1007/s00371-017-1354-0.
  • Zhang, X., T. Wang, J. Qi, H. Lu, and G. Wang (2018c). Progressive attention guided recurrent network for salient object detection. In Proceedings of the ieee conference on computer vision and pattern recognition, Salt Lake City, UT, USA (pp. 714–22).
  • Zhao, J.-X., -J.-J. Liu, D.-P. Fan, Y. Cao, J. Yang, and -M.-M. Cheng (2019). Egnet: Edge guidance network for salient object detection. In Proceedings of the ieee/cvf international conference on computer vision, Seoul, Korea (South) (pp. 8779–88).
  • Zhou, L., Z. Yang, Z. Zhou, and D. Hu. 2017. Salient region detection using diffusion process on a two-layer sparse graph. IEEE Transactions on Image Processing 26 (12):5882–94. doi:10.1109/TIP.2017.2738839.