Abstract
In this paper, we propose a novel algorithm for saliency object detection in unconstrained videos. Even though various methods have been proposed to solve this task, video saliency detection is still challenging due to the complication in object discovery as well as the utilization of motion cues. Most of existing methods adopt background prior to detect salient objects. However, they are prone to fail in the case that foreground objects are similar with the background. In this work, we aim to discover robust foreground priors as a complement to background priors so that we can improve the performance. Given an input video, we consider motion and appearance cues separately to generate initial foreground/background seeds. Then, we learn a global object appearance model using the initial seeds and remove unreliable seeds according to foreground likelihood. Finally, the seeds work as queries to rank all the superpixels in images to generate saliency maps. Experimental results on challenging public dataset demonstrate the advantage of our algorithm over state-of-the-art algorithms.
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Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: Computer Vision and Pattern Recognition (2014)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a survey (2014). arXiv preprint: arXiv:1411.5878
Born, R., Groh, J., Zhao, R., Lukasewycz, S.: Segregation of object and background motion in visual area MT: effects of microstimulation on eye movements. Neuron 26(3), 725–734 (2000)
Fang, Y., Chen, Z., Lin, W., Lin, C.W.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888–3901 (2012)
Fang, Y., Wang, Z., Lin, W., Fang, Z.: Video saliency incorporating spatiotemporal cues and uncertainty weighting. IEEE Trans. Image Process. 23(9), 3910–3921 (2014)
Fu, H., Cao, X., Tu, Z.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22(10), 3766–3778 (2013)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (2014)
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Guo, J., Li, Z., Cheong, L.F., Zhou, S.Z.: Video co-segmentation for meaningful action extraction. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2232–2239 (2013)
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)
Ko, B.C., Nam, J.Y.: Object-of-interest image segmentation based on human attention and semantic region clustering. JOSA A 23(10), 2462–2470 (2006)
Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 478–487 (2016)
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 724–732 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Sundaram, N., Brox, T., Keutzer, K.: Dense point trajectories by GPU-accelerated large displacement optical flow. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 438–451. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_32
Wang, W., Shen, J., Porikli, F.: Saliency-aware geodesic video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3395–3402 (2015)
Wang, W., Shen, J., Shao, L.: Consistent video saliency using local gradient flow optimization and global refinement. IEEE Trans. Image Process. 24(11), 4185–4196 (2015)
Xi, T., Zhao, W., Wang, H., Lin, W.: Salient object detection with spatiotemporal background priors for video. IEEE Trans. Image Process. 26, 3425–3436 (2017)
Xue, Y., Guo, X., Cao, X.: Motion saliency detection using low-rank and sparse decomposition. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1485–1488. IEEE (2012)
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013
Yu, H., Li, J., Tian, Y., Huang, T.: Automatic interesting object extraction from images using complementary saliency maps. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 891–894. ACM (2010)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: NIPS, vol. 3 (2003)
Zhou, T., Lu, Y., Di, H., Zhang, J.: Video object segmentation aggregation. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2016)
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Zhang, L., Lu, Y., Zhou, T. (2018). Automatic Foreground Seeds Discovery for Robust Video Saliency Detection. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_9
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DOI: https://doi.org/10.1007/978-3-319-77383-4_9
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