Abstract
In this paper, by observing the intrinsic sparsity of saliency map for the image, we propose a novel nonlocal \(L_{0}\) minimization framework to extract the sparse geometric structure of the saliency maps for the natural images. Specifically, we first propose to use the \(k\)-nearest neighbors of superpixels to construct a graph in the feature space. The novel \(L_{0}\)-regularized nonlocal minimization model is then developed on the proposed graph to describe the sparsity of saliency maps. Finally, we develop a first order optimization scheme to solve the proposed non-convex and discrete variational problem. Experimental results on four publicly available data sets validate that the proposed approach yields significant improvement compared with state-of-the-art saliency detection methods.
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Notes
- 1.
The notation \('\sharp '\) is a mathematical representation which stands for the cardinality of a set.
- 2.
We would like to list the running time (second per image) for the compared methods in our paper: Our 0.97s, CA 48.65s, CB 1.97s, LR 14.89s, CH 1.07s, MR 1.02s, MC 0.25s. It can be observed that our detector is comparable among all the MATLAB implementation based saliency detectors in our paper.
References
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. PAMI 34, 1915–1926 (2012)
Chang, K.Y., Liu, T.L., Chen, H.T., Lai, S.H.: Fusing generic objectness and visual saliency for salient object detection. In: ICCV (2011)
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, Part III. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012)
Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR (2012)
Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by ufo: Uniqueness, focusness and objectness. In: ICCV (2013)
Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. In: CVPR (2011)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS (2006)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. PAMI 20, 1254–1259 (1998)
Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: ACM Multimedia (2003)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR (2007)
Achanta, R., Estrada, F.J., 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)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR (2009)
Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: BMVC (2011)
Mai, L., Niu, Y., Liu, F.: Saliency aggregation: a data-driven approach. In: CVPR (2013)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. PAMI 33, 353–367 (2011)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. PAMI 34, 2274–2282 (2012)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22, 888–905 (2000)
Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process. Lett. 20, 637–640 (2013)
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR (2013)
Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: ICCV (2013)
Jiang, Z., Davis, L.S.: Submodular salient region detection. In: CVPR (2013)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs to model saliency in images. In: CVPR (2009)
Jia, Y., Han, M.: Category-independent object-level saliency detection. In: ICCV (2013)
Liu, R., Lin, Z., Shan, S.: Adaptive partial differential equation learning for visual saliency detection. In: CVPR (2014)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: NIPS (2011)
Liu, R., Lin, Z., Su, Z.: Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning. In: ACML (2013)
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via \(l_{0}\) gradient minimization. ACM TOG 30, 174 (2011)
Xu, L., Zheng, S., Jia, J.: Unnatural \(l_{0}\) sparse representation for natural image deblurring. In: CVPR (2013)
Pan, J., Su, Z.: Fast \(l_{0}\)-regularized kernel estimation for robust motion deblurring. IEEE Sig. Process. Lett. 20, 841–844 (2013)
Bougleux, S., Elmoataz, A., Melkemi, M.: Discrete regularization on weighted graphs for image and mesh filtering. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 128–139. Springer, Heidelberg (2007)
Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7, 1005–1028 (2008)
Einhäuser, W., König, P.: Does luminance-contrast contribute to a saliency map for overt visual attention? Eur. J. Neurosci. 17, 1089–1097 (2003)
Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR (2012)
Xie, Y., Lu, H.: Visual saliency detection based on bayesian model. In: ICIP (2011)
Bresson, X.: A short note for nonlocal tv minimization (2009)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR (2013)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACM Multimedia (2006)
Achanta, R., Susstrunk, S.: Saliency detection using maximum symmetric surround. In: ICIP (2010)
Acknowledgement
Risheng Liu is supported by the National Natural Science Foundation of China (No. 61300086), the China Postdoctoral Science Foundation (2013M530917, 2014T70249), the Fundamental Research Funds for the Central Universities (No. DUT12RC(3)67) and the Open Project Program of the State Key Laboratory of CAD&CG, Zhejiang University, Zhejiang, China (No. A1404). Zhixun Su is supported by National Natural Science Foundation of China (Nos. 61173103, 91230103) and National Science and Technology Major Project (No. 2013ZX04005021).
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Wang, Y., Liu, R., Song, X., Su, Z. (2015). Saliency Detection via Nonlocal \(L_{0}\) Minimization. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_35
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