Abstract
Human Visual System has the characters of focusing on salient regions when seeing images or videos. The method which finds the irregularity and unpredictability of images or videos by simulating human visual system is called saliency detection. In this paper, we propose a novel video saliency detection method based on temporal consistency. The traditional video detection approaches fall into two main groups. One processes a video frame by frame independently without considering motion information, and the other regards optical flow only as a part of features without taking account of consistency of video saliency between consecutive frames. In the proposed method, the temporal consistency constraint is enforced by using motion vectors. By constructing correspondences via motion information, the saliency map of each frame can be predicted by the result of its previous frame. By combining the predicted results and the traditional approaches, our algorithm can achieve better video saliency maps.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009., pp. 1597–1604. IEEE (2009)
Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409–416. IEEE (2011)
Cui, X., Liu, Q., Metaxas, D.: Temporal spectral residual: fast motion saliency detection. In: Proceedings of the ACM International Conference on Multimedia (2009)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, vol. 19, p. 545 (2007)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing 13(10), 1304–1318 (2004)
Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 631–637. IEEE (2005)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Li, H., Ngan, K.N.: Saliency model-based face segmentation and tracking in head-and-shoulder video sequences. Journal of Visual Communication and Image Representation 19(5), 320–333 (2008)
Liu, C., Yuen, P., Qiu, G.: Object motion detection using information theoretic spatio-temporal saliency. Pattern Recognition 42(11), 2897–2906 (2009)
Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II–302. IEEE (2004)
Slowe, T.E., Marsic, I.: Saliency-based visual representation for compression. In: Proceedings of International Conference on Image Processing 1997, vol. 2, pp. 554–557. IEEE (1997)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1999, vol. 2, IEEE (1999)
Sun, D., Roth, S., Black, M.: Secrets of optical flow estimation and their principles. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)
Tuzel, O., Porikli, F., Meer, P.: A bayesian approach to background modeling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, CVPR Workshops, pp. 58–58. IEEE (2005)
Wolf, L., Guttmann, M., Cohen-Or, D.: Non-homogeneous content-driven video-retargeting. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–6. IEEE (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Q., Chen, S., Zhang, B. (2012). Predictive Video Saliency Detection. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)