Abstract
Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal (RGB-T), may be an effective way for boosting saliency detection performance. The current research in this direction, however, is limited by the lack of a comprehensive benchmark. This work contributes such a RGB-T image dataset, which includes 821 spatially aligned RGB-T image pairs and their ground truth annotations for saliency detection purpose. With this benchmark, we propose a novel approach, graph-based multi-task manifold ranking algorithm, for RGB-T saliency detection. Extensive experiments against the baseline methods on the benchmark dataset demonstrate the effectiveness of the proposed approach.
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Notes
- 1.
The RGB-T dataset’s webpage: https://drive.google.com/file/d/0B4fH4G1f-jjNR3NtQUkwWjFFREk/view.
References
Conaire, C.O., O’Connor, N., Cooke, E., Smeaton, A.F.: Comparison of fusion methods for thermo-visual surveillance tracking. In: Proceedings of the International Conference on Information Fusion (2006)
Desingh, K., Krishna, K.M., Rajan, D., Jawahar, C.: Depth really matters: improving visual salient region detection with depth. In: BMVC (2013)
Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4) (2013)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE Trans. Image Process. 19(12), 3232–3242
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of the Advances in Neural Information Processing Systems (2006)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)
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)
Li, C., Yuan, Y., Cai, W., **a, Y., Dagan Feng, D.: Robust saliency detection via regularized random walks ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Li, C., Cheng, H., Hu, S., Liu, X., Tang, J., Lin, L.: Learning collaborative sparse representation for grayscale-thermal tracking. IEEE Trans. Image Process. 25(12), 5743–5756 (2016)
Peng, H., Li, B., **ong, W., Hu, W., Ji, R.: RGBD salient object detection: a benchmark and algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 92–109. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_7
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 (2015)
Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., Yang, Q.: RGBD salient object detection via deep fusion. IEEE Trans. Image Process. 26(5), 2274–2285 (2017)
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). https://doi.org/10.1007/978-3-642-15555-0_27
Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 15 (2009)
Tavakoli, H.R., Rahtu, E., Heikkilä, J.: Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 666–675. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_62
Torralba, A., Efros, A.: Unbiased look at dataset bias. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)
Tu, W.C., He, S., Yang, Q., Chien, S.Y.: Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Wang, Q., Zheng, W., Piramuthu, R.: Grab: visual saliency via novel graph model and background priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 535–543 (2016)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)
Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. IEEE Sig. Process. Lett. 20(7), 637–640 (2013)
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 (2013)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Scholkopf, B.: Ranking on data manifolds. In: Proceedings of Neural Information Processing Systems (2004)
Acknowledgement
This work was supported in part by the Natural Science Foundation of Anhui Higher Education Institution of China under Grants KJ2017A017, and in part by the National Natural Science Foundation of China under Grants No. 61602006 and No. 61671018, and in part by the Co-Innovation Center for Information Supply & Assurance Technology, Anhui University under Grant Y01002449.
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Wang, G., Li, C., Ma, Y., Zheng, A., Tang, J., Luo, B. (2018). RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and a Novel Approach. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_36
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