iccv iccv2013 iccv2013-372 iccv2013-372-reference knowledge-graph by maker-knowledge-mining
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Author: Xiaohui Li, Huchuan Lu, Lihe Zhang, Xiang Ruan, Ming-Hsuan Yang
Abstract: In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction errors. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models are constructed. For each image region, we first compute dense and sparse reconstruction errors. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, pixel-level saliency is computed by an integration of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise.
[1] R. Achanta, F. Estrada, P. Wils, and S. Susstrunk. Salient region detection and segmentation. In ICVS, pages 66–75, 2008.
[2] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequencytuned salient region detection. In CVPR, pages 1597–1604, 2009.
[3] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. Slic superpixels. Technical Report 149300, EPFL, 2010.
[4] A. Borji and L. Itti. Exploiting local and global patch rarities for saliency detection. In CVPR, pages 478–485, 2012.
[5] A. Borji, D. N. Sihite, and L. Itti. Salient object detection: A benchmark. In ECCV, 2012.
[6] K.-Y. Chang, T.-L. Liu, H.-T. Chen, and S.-H. Lai. Fusing generic objectness and visual saliency for salient object detection. In ICCV, pages 914–921, 2011.
[7] M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu. Global contrast based salient region detection. In CVPR, pages 409– 416, 2011.
[8] L. Duan, C. Wu, J. Miao, L. Qing, and Y. Fu. Visual saliency detection by spatially weighted dissimilarity. In CVPR, pages 473–480, 2011.
[9] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, pages 2376–2383, 2010.
[10] J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. In NIPS, 2006.
[11] X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In CVPR, 2007.
[12] L. Itti and C. Koch. Computational modeling of visual attention. Nature Reviews Neuroscience, 2(3): 194–201, 2001 .
[13] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 20: 1254–1259, 1998.
[14] H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li. Automatic salient object segmentation based on context and shape prior. In BMVC, 2011.
[15] T. Liu, J. Sun, N.-N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. In CVPR, 2007.
[16] V. Movahedi and J. H. Elder. Design and perceptual validation of performance measures for salient object segmentation. In POCV, pages 49–56, 2010.
[17] F. Perazzi, P. Krahenbuhl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In CVPR, pages 733–740, 2012.
[18] E. Rahtu, J. Kannala, M. Salo, and J. Heikkil a¨. Segmenting salient objects from images and videos. In ECCV, pages 366–379, 2010.
[19] X. Shen and Y. Wu. A unified approach to salient object detection via low rank matrix recovery. In CVPR, pages 853–860, 2012.
[20] J. Sun, H. Lu, and S. Li. Saliency detection based on integration of boundary and soft-segmentation. In ICIP, pages 1085–1088, 2012.
[21] Y. Wei, F. Wen, W. Zhu, and J. Sun. Geodesic saliency using background priors. In ECCV, pages 29–42, 2012.
[22] Y. Xie, H. Lu, and M.-H. Yang. Bayesian saliency via low and mid level cues. TIP, 22(5): 1689–1698, 2013.
[23] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang. Saliency detection via graph-based manifold ranking. In CVPR, 2013.
[24] M. Yifeng and Z. Hejie. Contrast-based image attention analysis by using fuzzy growing. In ACM Multimedia, pages 374–381, 2003.
[25] Y. Zhai and M. Shah. Visual attention detection in video sequences using spatiotemporal cues. In ACM Multimedia, pages 815–824, 2006. 2983