Abstract
A novel and outstanding saliency detection approach based on color features and background prior is proposed in this paper. Specifically, background prior is used in saliency detection widely, which considers the image boundaries as part of background. Then we propose an extended manifold ranking (EMR) algorithm to propagate the background prior to other image regions. Compared with GMR, EMR eliminates the negative effect of the initial assumption that non-boundary areas are all saliency regions. Furthermore, gradient boosting decision tree (GBDT) is introduced to refine the saliency map generated by EMR. The experimental results on three benchmark datasets demonstrate that our algorithm outperforms 10 state-of-the-art methods based on low-level features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiang, H., Wang, J., Yuan, Z., et al.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the CVPR, pp. 2083–2090 (2013)
Yang, C., Zhang, L., Lu, H., et al.: Saliency detection via graph-based manifold ranking. In: Proceedings of the CVPR, pp. 3166–3173 (2013)
Cheng, M.M., Zhang, G.X., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the CVPR, pp. 2814–2821 (2014)
Aytekin, C., Ozan, E.C., Kiranyaz, S., et al.: Visual saliency by extended quantum-cuts. In: Proceedings of the ICIP, pp. 1692–1696 (2015)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2000)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of the CVPR, pp. 1155–1162 (2013)
Achanta, R., Shaji, A., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: Proceedings of the CVPR, pp. 1597–1604 (2009)
Liu, Z., Zou, W., Le, M.O.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23(5), 1937–1952 (2014)
Kim, J., Han, D., Tai, Y.W., Kim, J.: Salient region detection via high-dimensional color transform. In: Proceedings of the CVPR, pp. 883–890 (2014)
Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the CVPR, pp. 110–119 (2015)
Zhou, L., Yang, Z., Yuan, Q., et al.: Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans. Image Process. 24(11), 3308–3320 (2015)
Tong, N., Lu, H., Xiang, R., et al.: Salient object detection via bootstrap learning. In: Proceedings of the CVPR, pp. 1884–1892 (2015)
Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)
Acknowledgement
This research was supported by the National Natural Science Foundation of China (Grant Nos. 11627802, 51678249), by the Science and Technology Projects of Guangdong (2013A011403003), and by the Science and Technology Projects of Guangzhou (201508010023).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, B., Gao, H., Liu, H. (2017). EMR: Extended Manifold Ranking for Saliency Detection. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-59081-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59080-6
Online ISBN: 978-3-319-59081-3
eBook Packages: Computer ScienceComputer Science (R0)