Abstract
In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of its intensity value to the mean intensity of the sub-window. The final saliency value of a given pixel is obtained as the sum of all saliency descriptors corresponding to this pixel. Any given pixel can be included by one or more random sub-windows. The recall-precision performance of the proposed saliency map is comparable to other existing saliency maps for the task of salient region detection. It also achieves state-of-the-art performance for the task of eye gaze prediction in terms of receiver operating characteristics.
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
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. Pattern Analysis Machine Intelligence 20(11), 1254–1259 (1998)
Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Processing 19(1), 185–198 (2010)
Rothenstein, A.L., Tsotsos, J.K.: Attention links sensing to recognition. Image and Vision Computing 26(1), 114–126 (2008)
Elazary, L., Itti, L.: A Bayesian model for efficient visual search and recognition. Vision Research 50(14), 1338–1352 (2010)
Moosmann, F., Larlus, D., Jurie, F.: Learning Saliency Maps for Object Categorization. In: ECCV International Workshop on The Representation and Use of Prior Knowledge in Vision (2006)
Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Frequency tuned Salient Region Detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (2009)
Buschman, T.J., Miller, E.K.: Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices. Science 315(5820), 1860–1862 (2007)
Seo, H.J., Milanfar, P.: Static and Space-time Visual Saliency Detection by Self- Resemblance. Journal of Vision 9(12), 1–27 (2009)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: A Bayesian Framework for Saliency Using Natural Statistics. Journal of Vision 8(7), 1–20 (2008)
Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Processing 19(1), 185–198 (2010)
Cui, X., Liu, Q., Metaxas, D.: Temporal spectral residual: fast motion saliency detection. In: ACM International Conference on Multimedia, pp. 617–620 (2009)
Rosin, P.L.: A simple method for detecting salient regions. Pattern Recognition 42(11), 2363–2371 (2009)
Achanta, R., Süsstrunk, S.: Saliency Detection using Maximum Symmetric Surround. In: IEEE International Conference on Image Processing (2010)
Vikram, T.N., Tscherepanow, M., Wrede, B.: A Random Center Surround Bottom up Visual Attention Model useful for Salient Region Detection. In: IEEE Workshop on Applications of Computer Vision, pp. 166–173 (2011)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Neural Information Processing Systems, pp. 545–552 (2007)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to Detect A Salient Object. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE International Conference on Computer Vision (2009)
Bruce, N.D., Tsotsos, J.K.: Attention based on Information Maximization. In: The International Conference on Computer Vision Systems (2007)
Mante, V., Frazor, R.A., Bonin, V., Geisler, W.S., Carandini, M.: Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience 8(12), 1690–1697 (2005)
Soltani, A., Koch, C.: Visual Saliency Computations: Mechanisms, Constraints, and the Effect of Feedback. Neuroscience 30(38), 12831–12843 (2010)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)
Gao, D., Mahadevan, V., Vasconcelos, N.: The discriminant center-surround hypothesis for bottom up saliency. In: Neural Information Processing Systems (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Narayan Vikram, T., Tscherepanow, M., Wrede, B. (2011). A Visual Saliency Map Based on Random Sub-window Means. In: Vitrià , J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)