Abstract
Saliency detection is an important problem in computer vision. Recently, graph-based manifold ranking (GMR) has been successfully employed in image saliency detection problem. Traditional GMR involves two main ranking stages, i.e., ranking with background queries and ranking with foreground queries. However, these two ranking stages are conducted separately which obviously ignores the correlation between background and foreground queries. Also, traditional GMR uses a single graph which lacks of considering multi-view features. To overcome these problems, in this paper, we propose a new multi-view synchronized manifold ranking for saliency detection problem. Our method aims to perform background and foreground ranking simultaneously by exploiting multiple kinds of features and thus performs more robustly and discriminatively for saliency detection problem. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed saliency detection method.
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References
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). https://doi.org/10.1007/978-3-540-79547-6_7
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Ssstrunk, S.: Slic superpixels. EPFL 149300 (2010)
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR, pp. 1–8 (2007)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)
Chang, K.Y., Liu, T.L., Chen, H.T., Lai, S.H.: Fusing generic objectness and visual saliency for salient object detection. In: ICCV, pp. 914–921 (2011)
Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)
Chuan, Y., Lihe, Z., Huchuan, L., Xiang, R., Ming-Hsuan, Y.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: CVPR, pp. 473–480 (2011)
Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 11 (2013)
Feng, Y., Xiao, J., Zhuang, Y., Liu, X.: Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 343–357. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_26
Guan, Y., Jiang, B., Xiao, Y., Tang, J., Luo, B.: A new graph ranking model for image saliency detection problem. In: IEEE International Conference on Software Engineering Research, Management and Applications, pp. 151–156 (2017)
Han, J., Zhang, D., Cheng, G., Guo, L., Ren, J.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote. Sens. 53(6), 3325–3337 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hou, X., Harel, J., Koch, C.: Image signature: highlighting sparse salient regions. IEEE TPAMI 34(1), 194–201 (2012)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20, 1254–1259 (1998)
Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing Markov chain. In: ICCV, pp. 1665–1672 (2013)
Jimei, Y., Ming-Hsuan, Y.: Top-down visual saliency via joint CRF and dictionary learning. In: CVPR, pp. 2296–2303 (2012)
Jonathan, H., Christof, K., Pietro, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)
Kanan, C., Tong, M.H., Zhang, L., Cottrell, G.W.: Sun: top-down saliency using natural statistics. Vis. Cogn. 17(6–7), 979–1003 (2009)
Li, C., Yuan, Y., Cai, W., Xia, Y., Feng, D.D.: Robust saliency detection via regularized random walks ranking. In: CVPR, pp. 2710–2717 (2015)
Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5455–5463 (2015)
Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 2976–2983 (2013)
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: CVPR, pp. 1139–1146 (2013)
Movahedi, V., Elder, J.H.: Design and perceptual validation of performance measures for salient object segmentation. In: CVPRW, pp. 49–56 (2010)
Murray, N., Vanrell, M., Otazu, X., Parraga, C.A.: Saliency estimation using a non-parametric low-level vision model. In: CVPR, pp. 433–440 (2011)
Peng, Q., Cheung, Y.M., You, X., Tang, Y.Y.: A hybrid of local and global saliencies for detecting image salient region and appearance. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–12 (2017)
Perazzi, F., ähenb ühl, P.K., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)
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
Ravi, A., Sheila, H., Francisco, E., Sabine, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)
Rezazadegan Tavakoli, H., Rahtu, E., Heikkilä, J.: Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: SCIA, pp. 666–675 (2011)
Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 15 (2009)
Tong, N., Lu, H., Zhang, L., Ruan, X.: Saliency detection with multi-scale superpixels. IEEE Signal Process. Lett. 21(9), 1035–1039 (2014)
Tu, W.C., He, S., Yang, Q., Chien, S.Y.: Real-time salient object detection with a minimum spanning tree. In: CVPR, pp. 2334–2342 (2016)
Wang, Q., Zheng, W., Piramuthu, R.: Grab: visual saliency via novel graph model and background priors. In: CVPR, pp. 535–543 (2016)
Wang, Z., Ren, J., Zhang, D., Sun, M., Jiang, J.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68–83 (2018)
Xiaodi, H., Liqing, Z.: Saliency detection: a spectral residual approach. In: CVPR, pp. 1–8 (2007)
Xie, Y., Lu, H., Yang, M.H.: Bayesian saliency via low and mid level cues. IEEE TIP 22(5), 1689–1698 (2013)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162 (2013)
Yan, Y., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recogn. 79, 65–78 (2018)
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), 32.1–32.20 (2008)
Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1265–1274 (2015)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR, pp. 2814–2821 (2014)
Acknowledgments
This work was supported by the National Nature Science Foundation of China (61602001, 61502006, 61402002); Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2016A020, KJ2018A0023), Natural science foundation of Anhui Province (1508085QF127).
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Guan, Y., Jiang, B., Zhang, Y., Zheng, A., Sun, D., Luo, B. (2018). Saliency Detection via Multi-view Synchronized Manifold Ranking. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_46
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