Abstract
Salient object detection in video has attracted enormous research efforts for its wide applicability. But there are still some issues in restraining the disturbance of background, which make it difficult to detect salient object in complex scenarios. Inspired by the hypothesis of center prior in image domain, we novelly introduced the concept of dynamic attention center in video. The distance between specific regions and this center is used as a weighting term to restrain the influence of background disturbance and obtain more accurate spatial and temporal saliency maps. Besides, we develop a dynamic fusion method to combine the temporal and spatial saliency map, leading to higher spatiotemporal consistency. The experiments on Freiburg-Berkeley Motion Segmentation Dataset show that our method outperforms several state-of-art methods on subjective visual perception and objective measurements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12(1), 97C136 (1980)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE trans pami. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Niebur, E., Koch, C.: Computational architectures for attention. In: The Attentive (1998)
Borji, A., et al.: Salient Object Detection: A Survey (2014). eprint arXiv:1411.5878
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1597–1604 (2009)
Zhang, J., Wang, M., Zhang, S., Li, X., Wu, X.: Spatio-chromatic context modeling for color saliency analysis. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1177–1189 (2016)
Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 569–582 (2011)
Chen, Y., Nguyen, T.V., Katti, H., Yadat, K., Kankanhalli, M., Yuan, J., Yan, S., Wang, M.: Audio matters in visual saliency. IEEE Trans. Circ. Syst. Video Technol. 24(11), 1992–2003 (2014)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Multimedia Proceedings of Annual ACM International Conference on Multimedia, vol. 2006, pp. 815–824 (2006)
Wang, M., Hong, R., Yuan, X.-T., Yan, S., Chua, T.-S.: Movie2comics: towards a lively video content presentation. IEEE Trans. Multimedia 14(3), 858–870 (2012)
Zhang, J., Wang, M., Gao, J., Wang, Y., Zhang, X., Wu, X.: Saliency detection with a deeper investigation of light field. In: International Joint Conference on Artificial Intelligence (IJCAI) (2015)
Fang, Y., Lin, W., Chen, Z., Tsai, C.-M., Lin, C.-W.: A video saliency detection model in compressed domain. IEEE Trans. Circ. Syst. Video Technol. 24(1), 27–38 (2014)
Fu, H., Cao, X., Tu, Z.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22, 3766–3778 (2013)
Zhou, F., Kang, S.B., Cohen, M.F.: Time-mapping using space-time saliency. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3358–3365 (2014)
Fang, Y., Wang, Z., Lin, W., Fang, Z.: Video saliency incorporating spatiotemporal cues and uncertainty weighting. IEEE Trans. Image Process. 23(9), 3910–3921 (2014)
Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: Proceedings of the British Machine Vision Conference (BMVC) (2011)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1351–1365 (2001)
Christoudias, C., Georgescu, B., Meer, P.: Synergism in low level vision. In: International Conference on Pattern Recognition, pp. 150–155 (2002)
Ochs, P., Malik, J., Brox, T.: Segmentation of moving objects by long term video analysis. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1187–1200 (2014)
Guo, C., Ma, Q., Zhang, L.: Spatiotemporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–8 (2008)
Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting Salient Objects from Images and Videos. In: European Conference on Computer Vision, pp. 366–379 (2010)
Acknowledgements
This work was partly supported by the National High Technology Research and Development Program of China (863 Program) No. 2015AA016306, the National Nature Science Foundation of China (No. 61231015) , the National Natural Science Foundation of China (61502348), the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Shao, M., Hu, R., Wang, X., Wang, Z., Xiao, J., Gao, G. (2016). Salient Object Detection in Video Based on Dynamic Attention Center. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-48896-7_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48895-0
Online ISBN: 978-3-319-48896-7
eBook Packages: Computer ScienceComputer Science (R0)