Abstract
A visual attention system should preferentially locate the most informative spots in complex environments. In this paper, we propose a novel attention model to produce saliency maps by generating information distributions on incoming images. Our model automatically marks spots with large information amount as saliency, which ensures the system gains the maximum information acquisition through attending these spots. By building a biological computational framework, we use the neural coding length as the estimation of information, and introduce relative entropy to simplify this calculation. Additionally, a real attention system should be robust to scales. Inspired by the visual perception process, we design a hierarchical framework to handle multi-scale saliency. From experiments we demonstrated that the proposed attention model is efficient and adaptive. In comparison to mainstream approaches, our model achieves better accuracy on fitting human fixations.
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
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45(2), 83–105 (2001)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, vol. 18, pp. 155–162 (2006)
Maunsell, J.H., Treue, S.: Feature-based attention in visual cortex. Trends in Neurosciences 29(6), 317–322 (2006)
Gao, D., Vasconcelos, N.: Decision-theoretic saliency: Computational principles, biological plausibility, and implications for neurophysiology and psychophysics. Neural computation 21(1), 239–271 (2009)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach, pp. 1–8 (2007)
Bruce, N., Tsotsos, J.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 5 (2009)
Hou, X., Zhang, L.: Dynamic visual attention: searching for coding length increments. In: Advances in Neural Information Processing Systems, vol. 21, pp. 681–688 (2009)
Boiman, O.: Detecting irregularities in images and in video. International Journal of Computer Vision 74, 17–31 (2007)
Balasubramanian, V., Kimber, D., Berry II, M.J.: Metabolically efficient information processing. Neural Computation 13(4), 799–815 (2001)
Wainwright, M.J.: Visual adaptation as optimal information transmission. Vision Research 39(23), 3960–3974 (1999)
Zhang, L., Tong, M., Marks, T., Shan, H., Cottrell, G.: SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision 8(7), 32 (2008)
van Hateren, J.H.: Real and optimal neural images in early vision. Nature 360, 68–70 (1992)
Intriligator, J., Cavanagh, P.: The spatial resolution of visual attention. Cognitive Psychology 43(3), 171–216 (2001)
Tatler, B.W., Baddeley, R.J., Gilchrist, I.D.: Visual correlates of fixation selection: effects of scale and time. Vision Research 45(5), 643–659 (2005)
Deco, G., Schurmann, B.: A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision Research 40(20), 2845–2859 (2000)
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field propertiesby learning a sparse code for natural images. Nature 381, 607–609 (1996)
Bell, A.J., Sejnowski, T.J.: The independent components of natural scenes are edge filters. Vision Research 37(23), 3327–3338 (1997)
Hyvarinen, A., Hoyer, P., Hurri, J., Gutmann, M.: Statistical models of images and early vision. In: Proceedings of the Int. Symposium on Adaptive Knowledge Representation and Reasoning, pp. 1–14 (2005)
Gao, D., Mahadevan, V., Vasconcelos, N.: The discriminant center-surround hypothesis for bottom-up saliency. In: Advances in Neural Information Processing Systems, vol. 20, pp. 497–504 (2008)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. In: Advances in Neural Information Processing Systems, vol. 18, pp. 547–554 (2006)
Gao, D., Vasconcelos, N.: Discriminant saliency for visual recognition from cluttered scenes. In: Advances in Neural Information Processing Systems, vol. 17, pp. 481–488 (2005)
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
Cao, Y., Zhang, L. (2010). A Novel Hierarchical Model of Attention: Maximizing Information Acquisition. 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_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-12307-8_21
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)