Abstract
One of the most important and essential parts of image processing tasks in computer vision is to predict the regions of interest that is the most attractive and also representative salient ones. This task can be realized effortless by the human visual system via the function of selective attention. In this paper, we introduced a set of new visual features, including contract, entropy, and local feature change into the Itti bottom-up model. We used a set of qualitative and quantitative analysis to demonstrate the effectiveness of the proposed approach on a large dataset with different scenes, and the results show that the modified Itti model combined with the new features can improve greatly the efficiency of salient region detection in real scenes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)
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)
Walther, D., Koch, C.: Saliency Toolbox 2.0. (2006)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: CVPR, pp. 1–8. IEEE (2007)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, vol. 18, p. 155 (2006)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, vol. 19, p. 545 (2007)
Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: Foveated analysis of image features at fixations. Vision Research 47(25), 3160–3172 (2007)
Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: GAFFE: A gaze-attentive fixation finding engine. IEEE Transactions on Image Processing 17(4), 564–573 (2008)
Parkhurst, D.J., Niebur, E.: Scene content selected by active vision. Spatial Vision 16(2), 125–154 (2003)
Privitera, C.M., Stark, L.W.: Algorithms for defining visual regions-of-interest: Comparison with eye fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(9), 970–982 (2000)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113. IEEE (2009)
Kootstra, G., Nederveen, A., De Boer, B.: Paying attention to symmetry. In: Proceedings of the British Machine Vision Conference, pp. 1115–1125 (2008)
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, S., Li, Y. (2012). A Modified Selective Attention Model for Salient Region Detection in Real Scenes. 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_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_29
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)