Abstract
In this paper, we come up with a bottom-up saliency algorithm that both consider the background and foreground cues. First, we compute the coarse saliency map by manifold ranking on a graph using partly image boundaries which consider as background prior. In this step, we just select left and top sides as background seeds. Second, bi-segment the preliminary saliency map to extract foreground information. Third, we utilize Markov absorption probabilities to highlight objects against the background. Results on public datasets show that our proposed method achieve fabulous performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rabinovich, A., Vedaldi, A., Galleguillos, C., et al.: Objects in context. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (2007)
Rutishauser, U., Walther, D., Koch, C., et al.: Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004, vol. 2, pp. II-37–II-44. IEEE (2004)
Fang, Y., Chen, Z., Lin, W., et al.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888–3901 (2012)
Wang, X.J., Ma, W.Y., Li, X.: Data-driven approach for bridging the cognitive gap in image retrieval. In: 2004 IEEE International Conference on Multimedia and Expo, 2004, ICME 2004, vol. 3, pp. 2231–2234. IEEE (2004)
Yang, C., Zhang, L., Lu, H., et al.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE Trans. Image Process. 19(12), 3232–3242 (2010)
Li, H., Lu, H., Lin, Z., et al.: Inner and inter label propagation: salient object detection in the wild. IEEE Trans. Image Process. 24(10), 3176–3186 (2015)
Zhu, W., Liang, S., Wei, Y., et al.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)
Xie, Y., Lu, H., Yang, M.H.: Bayesian saliency via low and mid level cues. IEEE Trans. Image Process. 22(5), 1689–1698 (2013)
Tong, N., Lu, H., Ruan, X., et al.: Salient object detection via bootstrap learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1884–1892 (2015)
Achanta, R., Shaji, A., Smith, K., et al.: Slic superpixels (2010)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2006)
Yang, J., Yang, M.H.: Top-down visual saliency via joint CRF and dictionary learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2296–2303. IEEE (2012)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 73–80. IEEE (2010)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. In: Matters of Intelligence, pp. 115–141. Springer, Netherlands (1987)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)
Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: CVPR 2009, IEEE Conference on Computer Vision and Pattern Recognition 2009, pp. 1597–1604. IEEE (2009)
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. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_3
Zhang, L., Tong, M.H., Marks, T.K., et al.: SUN: a Bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32 (2008)
Cheng, M.M., Mitra, N.J., Huang, X., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Sun, J., Lu, H., Liu, X.: Saliency region detection based on Markov absorption probabilities. IEEE Trans. Image Process. 24(5), 1639–1649 (2015)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Yan, Q., Xu, L., Shi, J., et al.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1139–1146 (2013)
Tong, N., Lu, H., Zhang, L., et al.: Saliency detection with multi-scale superpixels. IEEE Sig. Process. Lett. 21(9), 1035–1039 (2014)
Cheng, M.M., Warrell, J., Lin, W.Y., et al.: Efficient salient region detection with soft image abstraction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)
Li, X., Lu, H., Zhang, L., et al.: Saliency detection via dense and sparse reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2976–2983 (2013)
Grinstead, C.M., Snell, J.L.: Introduction to Probability. American Mathematical Society, London (2012)
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)
Perazzi, F., Krähenbühl, P., Pritch, Y., et al.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733–740. IEEE (2012)
Chang, K.Y., Liu, T.L., Chen, H.T., et al.: Fusing generic objectness and visual saliency for salient object detection. In: 2011 International Conference on Computer Vision, pp. 914–921. IEEE (2011)
Acknowledgement
The research was partly supported by the program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, USST incubation project (15HJPY-MS02), National Natural Science Foundation of China (No. U1304616, No. 61502220).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lin, X., Yan, Z., Jiang, L. (2017). Saliency Detection via Foreground and Background Seeds. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_18
Download citation
DOI: https://doi.org/10.1007/978-981-10-4154-9_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4153-2
Online ISBN: 978-981-10-4154-9
eBook Packages: EngineeringEngineering (R0)