Abstract
Salient region detection has gained a great deal of attention in computer vision. It is useful for applications such as adaptive video/image compression, image segmentation, anomaly detection, image retrieval, etc. In this paper, we study saliency detection using a center-surround approach. The proposed method is based on estimating saliency of local feature contrast in a Bayesian framework. The distributions needed are estimated particularly using sparse sampling and kernel density estimation. Furthermore, the nature of method implicitly considers what refereed to as center bias in literature. Proposed method was evaluated on a publicly available data set which contains human eye fixation as ground-truth. The results indicate more than 5% improvement over state-of-the-art methods. Moreover, the method is fast enough to run in real-time.
Chapter 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 Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Achanta, R., Estrada, F.J., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008), http://icvs2008.info/index.htm
Seo, H.J., Milanfar, P.: Training-free, generic object detection using locally adaptive regression kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1688–1704 (2010)
Rahtu, E., Heikkilä, J.: A simple and efficient saliency detector for background subtraction. In: Proc. the 9th IEEE International Workshop on Visual Surveillance (VS 2009), Kyoto, Japan, pp. 1137–1144 (2009), http://www.ee.oulu.fi/mvg/page/saliency
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008), doi:10.1109/CVPR.2008.4587715
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007), doi:10.1109/CVPR.2007.383267
Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned Salient Region Detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Miami Beach, Florida (2009), http://www.cvpr2009.org/
Tsotsos, J.K., Bruce, N.D.B.: Saliency based on information maximization. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems 18, pp. 155–162. MIT Press, Cambridge (2006)
Lin, Y., Fang, B., Tang, Y.: A computational model for saliency maps by using local entropy. In: AAAI Conference on Artificial Intelligence (2010)
Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 171–177 (2010)
Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010), http://www.ee.oulu.fi/mvg/page/saliency
Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8(7) (2008), http://www.journalofvision.org/content/8/7/13.abstract , doi:10.1167/8.7.13
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) (2008), http://www.journalofvision.org/content/8/7/32.abstract , doi:10.1167/8.7.32
Yang, Y., Song, M., Li, N., Bu, J., Chen, C.: What is the chance of happening: A new way to predict where people look. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 631–643. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-15555-0_46
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE International Conference on Computer Vision, ICCV (2009)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2376–2383 (2010), doi:10.1109/CVPR.2010.5539929
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
Rezazadegan Tavakoli, H., Rahtu, E., Heikkilä, J. (2011). Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-21227-7_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21226-0
Online ISBN: 978-3-642-21227-7
eBook Packages: Computer ScienceComputer Science (R0)