Abstract
In this paper, we propose a novel approach to detect visual saliency based on spread pattern and manifold ranking. We firstly construct a close-loop graph model with image superpixels as nodes. The saliency of each node is defined by its relevance to given queries according to graph-based manifold ranking technique. Unlike existing methods which choose a few background and foreground queries in a two-stage scheme, we propose to treat each node as a potential foreground query by assigning to it an initial ranking score based on its spread pattern property. The new concept spread pattern represents how the ranking score of one node is propagated to the whole graph. An accurate query map is generated accordingly, which is then used to produce the final saliency map with manifold ranking. Our method is computationally efficient and outperforms the state-of-the-art methods.
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References
Chen, T., et al.: Sketch2Photo: internet image montage. ACM Transactions on Graphics (TOG)Â 28(5) (2009)
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing 13(10), 1304–1318 (2004)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)
Kanan, C., Cottrell, G.: Robust classification of objects, faces, and flowers using natural image statistics. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2010)
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)
Achanta, R., et al.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. IEEE (2009)
Cheng, M.-M., et al.: Global contrast based salient region detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2011)
Perazzi, F., et al.: Saliency filters: Contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2012)
Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012)
Yang, C., et al.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
Judd, T., et al.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE (2009)
Jiang, H., et al.: Automatic salient object segmentation based on context and shape prior. BMVCÂ 3(4) (2011)
Zhou, D., et al.: Ranking on data manifolds. In: NIPS, vol. 3 (2003)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2011)
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Huang, Y., Fu, K., Yao, L., Wu, Q., Yang, J. (2014). Saliency Detection Based on Spread Pattern and Manifold Ranking. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_29
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DOI: https://doi.org/10.1007/978-3-662-45646-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
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