Abstract
By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method - the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency.
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
Preview
Unable to display preview. Download preview PDF.
References
Cavanaugh, J.R., Bair, W., Movshon, J.A.: Nature and interaction of signals from the receptive field center and surround in macaque v1 neurons. Journal of Neurophysiol. 88, 2530–2546 (2002)
Allman, J., Miezin, F., McGuinness, E.: Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. Annual Review of Neuroscience 8, 407–430 (1985)
Vinje, W.E., Gallant, J.L.: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456), 1273–1276 (2000)
Rensink, R.A.: Seeing, sensing, and scrutinizing. Vision Research 40(10-12), 1469–1487 (2000)
Rensik, R.A., Enns, J.T.: Preemption effects in visual search: Evidence for low-level grouping. Psychological Review 102, 101–130 (1995)
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)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10-12), 1489–1506 (2000)
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, June 2005, vol. 1, pp. 631–637 (2005)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems 19, pp. 545–552. MIT Press, Cambridge (2007)
Gao, D., Vasconcelos, N.: Bottom-up saliency is a discriminant process. In: ICCV, pp. 1–6 (2007)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems 18, pp. 155–162. MIT Press, Cambridge (2006)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2007, June 2007, pp. 1–8 (2007)
Mahadevan, V., Vasconcelos, N.: Background subtraction in highly dynamic scenes. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, June 2008, pp. 1–6 (2008)
Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379C423, 623C656 (1948)
Ma, Y., Derksen, H., Hong, W., Wright, J.: Segmentation of multivariate mixed data via lossy data coding and compression. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9), 1546–1562 (2007)
Wright, J., Tao, Y., Lin, Z., Ma, Y., Shum, H.Y.: Classification via minimum incremental coding length (micl). In: Advances in Neural Information Processing Systems, vol. 20, pp. 1633–1640. MIT Press, Cambridge (2008)
Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (1989)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)
Wolfe, J.: Guided search 2.0, a revised model of guided search. Psychonomic Bulletin and Review 1(2), 202–238 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Zhou, Y., Yan, J., Niu, Z., Yang, J. (2010). Visual Saliency Based on Conditional Entropy. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-12307-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
eBook Packages: Computer ScienceComputer Science (R0)