Abstract
We propose a multi-layer graph based approach for salient object detection in natural images. Starting from a set of multi-scale image decomposition using superpixels, we propose an objective function optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. After isolating the object kernel, we enhance the accuracy of our saliency maps through an objectness-like based refinement approach. Beside its simplicity, our algorithm yields very accurate salient objects with clear boundaries. Experiments have shown that our approach outperforms several recent methods dealing with salient object detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE CVPR, pp. 1597–1604 (2009)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Sösstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)
Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE TPAMI 34(11), 2189–2202 (2012)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE TIP 19(12), 3232–3242 (2010)
Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.-H.: Saliency detection via absorbing Markov chain. In: IEEE ICCV, pp. 1665–1672 (2013)
Kim, J., Han, D., Tai, Y.W., Kim, J.: Salient region detection via high-dimensional color transform and local spatial support. IEEE TIP 25(1), 9–23 (2016)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. IEEE TPAMI 33(2), 353–367 (2011)
Liu, Z., Zou, W., Le Meur, O.: Saliency tree: a novel saliency detection framework. IEEE TIP 23(5), 1937–1952 (2014)
Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. In: IEEE CVPR Workshops, vol. 9(12), pp. 45–52 (2009)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: IEEE CVPR, pp. 1155–1162 (2013)
Yang, C., Zhang, L., Lu, H., Ruan, X.: Saliency detection via graph-based manifold ranking. In: IEEE CVPR, pp. 3166–3173 (2013)
Zhang, L., Gu, Z., Li, H.: SDSP: a novel saliency detection method by combining simple priors. In: IEEE ICIP, pp. 171–175 (2013)
Zhu, L., Klein, D.A., Frintrop, S., Cao, Z., Cremers, A.B.: A multi-size superpixel approach for salient object detection based on multivariate normal distribution estimation. IEEE TIP 23(12), 5094–5107 (2014)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: IEEE CVPR, pp. 2814–2821 (2014)
Zhou, D., Bousquet, O., Lal, T., Weston, J., Scholkopf, B.: Learning with local and global consistency. NIPS (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Filali, I., Allili, M.S., Benblidia, N. (2016). Multi-graph Based Salient Object Detection. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-41501-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41500-0
Online ISBN: 978-3-319-41501-7
eBook Packages: Computer ScienceComputer Science (R0)