Abstract
Different from the traditional images, 4D light field images contain the scene structure information and have been proved that can better obtain the saliency. Instead of estimating depth or using the unique refocusing capability, we proposed to obtain the occlusion relationship from the raw image to calculate saliency detection. The occlusion relationship is calculated using the Epipolar Plane Image (EPI) from the raw light field image which can distinguish a region is most likely a foreground or background. By analyzing the occlusion relationship in the scene, true edges of objects can be selected from the surface textures of objects, which is effective to segment the object completely. Moreover, we assume that objects which are non-occluded are more likely to be the foreground and objects that are occluded by lots of objects are background. Then the occlusion relationship is integrated into a modified saliency detection framework to obtain the saliency regions. Experiment results demonstrate that the occlusion relationship can help to improve the saliency detection accuracy, and the proposed method achieves significantly higher accuracy and robustness in comparison with state-of-the-art light field saliency detection methods.
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References
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 307–312 (2004)
Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 414–429 (2015)
Sun, J., Ling, H.: Scale and object aware image retargeting for thumbnail browsing. 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1511–1518. IEEE (2011)
Ding, Y., Xiao, J., Yu, J.: Importance filtering for image retargeting. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 89–96. IEEE (2011)
Cheng, M.-M., Warrell, J., Lin, W.-Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)
Jiang, H., Wang, J., Yuan, Z., Yang, W., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)
Jiang, Z., Davis, L.S.: Submodular salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2043–2050 (2013)
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
Li, N., Ye, J., Ji, Y., Ling, H., Yu, J.: Saliency detection on light field. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2806–2813 (2014)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)
Ng, R., Levoy, M., Brédif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Computer Science Technical Report CSTR, vol. 2, no. 11, pp. 1–11 (2005)
Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 110–119 (2015)
Achanta, R., Estrada, F., 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). doi:10.1007/978-3-540-79547-6_7
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597–1604. IEEE (2009)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, pp. 155–162 (2005)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th ACM International Conference on Multimedia, pp. 815–824. ACM (2006)
Itti, L., Koch, C., Niebur, E., et al.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2006)
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). doi:10.1007/978-3-642-15555-0_27
Xie, Y., Lu, H.: Visual saliency detection based on bayesian model. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 645–648. IEEE (2011)
Xie, Y., Huchuan, L., Yang, M.-H.: Bayesian saliency via low and mid level cues. IEEE Trans. Image Process. 22(5), 1689–1698 (2013)
Sun, J., Lu, H., Li, S.: Saliency detection based on integration of boundary and soft-segmentation. In: 2012 19th IEEE International Conference on Image Processing, pp. 1085–1088. IEEE (2012)
Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: 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)
Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 853–860. IEEE (2012)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)
Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: 2011 International Conference on Computer Vision, pp. 2214–2219. IEEE (2011)
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.-H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)
Zhang, X., Wang, Z., Yan, C., Zou, H., Peng, Q., Jiang, X., Dan, W.U.: Animal Nutrition Institute, and Sichuan Agricultural University, “Saliency optimization from robust background detection”. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: a bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32–32 (2008)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look, vol. 30, no. 2, pp. 2106–2113 (2009)
Yang, J.: Top-down visual saliency via joint CRF and dictionary learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Proceedings/CVPR. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 157, no. 10, pp. 1–1 (2012)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 409–416 (2015)
Cho, D., Lee, M., Kim, S., Tai, Y.W.: Modeling the calibration pipeline of the Lytro camera for high quality light-field image reconstruction. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 3280–3287 (2013)
Radhakrishna, A., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels, Dept. School Comput. Commun. Sci., EPFL, Lausanne, Switzerland. Technical report 149300 (2010)
Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.-M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
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Sheng, H., Feng, W., Zhang, S. (2016). Cellular Automata Based on Occlusion Relationship for Saliency Detection. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_3
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DOI: https://doi.org/10.1007/978-3-319-47650-6_3
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