Abstract
In this paper we introduce a new method for Visual Saliency detection. The goal of our method is to emphasize regions that show rare visual aspects in comparison with those showing frequent ones. We propose a bottom up approach that performs a new technique based on low level image features (texture) analysis. More precisely, we use SIFT Density Maps (SDM), to study the distribution of keypoints into the image with different scales of observation, and its relationship with real fixation points. The hypothesis is that the image regions that show a larger distance from the mode (most frequent value) of the keypoints distribution over all the image are the same that better capture our visual attention. Results have been compared to two other low-level approaches and a supervised method.
Chapter PDF
Similar content being viewed by others
References
Constantinidis, C., Steinmetz, M.A.: Posterior parietal cortex automatically encodes the location of salient stimuli. The Journal of Neuroscience 25(1), 233–238 (2005)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 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)
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)
Luo, J.: Subject content-based intelligent cropping of digital photos. In: IEEE International Conference on Multimedia and Expo (2007)
Sundstedt, V., Chalmers, A., Cater, K., Debattista, K.: Topdown visual attention for efficient rendering of task related scenes. In: In Vision, Modeling and Visualization, pp. 209–216 (2004)
Itti, L., Koch, C.: Computational modeling of visual attention. Nature Reviews Neuroscience 2(3) (2001)
Chen, L.-Q., Xie, X., Fan, X., Ma, W.-Y., Zhang, H.-J., Zhou, H.-Q.: A visual attention model for adapting images on small displays. ACM Multimedia Systems Journal 9(4) (2003)
Judd, Y., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE 12th International Conference on Computer Vision, pp. 2106–2133 (2009)
http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
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
Ardizzone, E., Bruno, A., Mazzola, G. (2011). Visual Saliency by Keypoints Distribution Analysis. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24085-0_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-24085-0_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24084-3
Online ISBN: 978-3-642-24085-0
eBook Packages: Computer ScienceComputer Science (R0)
