iccv iccv2013 iccv2013-91 iccv2013-91-reference knowledge-graph by maker-knowledge-mining
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Author: Xi Li, Yao Li, Chunhua Shen, Anthony Dick, Anton Van_Den_Hengel
Abstract: Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel’s (or region ’s) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on centerversus-surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the stateof-the-art approaches to salient object detection.
[1] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell., 20(1 1): 1254– 1259, 1998.
[2] N. Bruce and J. Tsotsos. Saliency based on information maximization. In Proc. Adv. Neural Inf. Process. Syst., 2005.
[3] X. Hou and L. Zhang. Dynamic visual attention: searching for coding length increments. In Proc. Adv. Neural Inf. Process. Syst., pages 681–688, 2008.
[4] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 1597– 1604, 2009.
[5] Y. Wei, F. Wen, W. Zhu, and J. Sun. Geodesic saliency using background priors. In Proc. Eur. Conf. Comp. Vis., pages 29–42, 2012.
[6] J. Feng, Y. Wei, L. Tao, C. Zhang, and J. Sun. Salient object detection by composition. In Proc. IEEE Int. Conf. Comp. Vis., pages 1028–1035, 2011.
[7] M. Cheng, G. Zhang, N. Mitra, X. Huang, and S. Hu. Global contrast based salient region detection. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 409–416, 2011.
[8] T. Liu, J. Sun, N. Zheng, X. Tang, and H. Shum. Learning to detect a salient object. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2007.
[9] D. Klein and S. Frintrop. Center-surround divergence of feature statistics for salient object detection. In Proc. IEEE Int. Conf. Comp. Vis., pages 2214–2219, 2011.
[10] B. Alexe, T. Deselaers, and V. Ferrari. What is an object? In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 73–80, 2010.
[11] F. Perazzi, P. Krahenbuhl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 733–740, 2012.
[12] X. Shen and Y. Wu. A unified approach to salient object detection via low rank matrix recovery. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 853–860, 2012.
[13] H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li. Automatic salient object segmentation based on context and shape prior. In Proc. Brit. Mach. Vis. Conf., 2011.
[14] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 2376–2383, 2010.
[15] K. Chang, T. Liu, H. Chen, and S. Lai. Fusing generic objectness and visual saliency for salient object detection. In Proc. IEEE Int. Conf. Comp. Vis., pages 914–921, 2011.
[16] E. Rahtu, J. Kannala, M. Salo, and J. Heikkil a¨. Segmenting salient objects from images and videos. In Proc. Eur. Conf. Comp. Vis., pages 366–379, 2010.
[17] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang. Saliency detection via graph-based manifold ranking. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 3166–3173, 2013.
[18] J. Sun and H. Ling. Scale and object aware image retargeting for thumbnail browsing. In Proc. IEEE Int. Conf. Comp. Vis., pages 1511–15 18, 2011.
[19] G. Sharma, F. Jurie, and C. Schmid. Discriminative spatial saliency for image classification. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 3506–3513, 2012.
[20] L. Wang, J. Xue, N. Zheng, and G. Hua. Automatic salient object extraction with contextual cue. In Proc. IEEE Int. Conf. Comp. Vis., pages 105–1 12, 2011.
[21] Y. Lu, W. Zhang, H. Lu, and X. Xue. Salient object detection using concavity context. In Proc. IEEE Int. Conf. Comp. Vis., pages 233–240, 2011.
[22] D. Gao, V. Mahadevan, and N. Vasconcelos. The discriminant center-surround hypothesis for bottom-up saliency. In Proc. Adv. Neural Inf. Process. Syst., 2007.
[23] D. H. Hubel and T. N. Wiesel. Receptive fields and functional architecture in two nonstriate visual areas. J. Neurophysiology, 28:229–289, 1965.
[24] J. A. K. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle. Weighted
[25]
[26]
[27]
[28]
[29]
[30] [3 1]
[32]
[33] least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1):85–105, 2002. T. Malisiewicz, A. Gupta, and A. Efros. Ensemble of exemplar-svms for object detection and beyond. In Proc. IEEE Int. Conf. Comp. Vis., pages 89–96, 2011. D. Zhou, J. Huang, and B. Scholk¨ opf. Learning with hypergraphs: Clustering, classification, and embedding. In Proc. Adv. Neural Inf. Process. Syst., 2007. X. Yuan, B. Hu, and R. He. Agglomerative mean-shift clustering. IEEE Trans. on Knowledge and Data Engineering, 24(2):209–219, 2012. V. Movahedi and J. Elder. Design and perceptual validation of performance measures for salient object segmentation. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. Workshops, pages 49–56, 2010. S. Alpert, M. Galun, R. Basri, and A. Brandt. Image segmentation by probabilistic bottom-up aggregation and cue integration. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 1–8, 2007. A. Borji, D. Sihite, and L. Itti. Salient object detection: A benchmark. In Proc. Eur. Conf. Comp. Vis., pages 414–429, 2012. J. Li, M. Levine, X. An, X. Xu, and H. He. Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell., 35:996–1010, 2013. A. Rosenfeld and D. Weinshall. Extracting foreground masks towards object recognition. In Proc. IEEE Int. Conf. Comp. Vis., pages 1371–1378, 2011. X. Ren and L. Bo. Discriminatively trained sparse code gradients for contour detection. In Adv. Neural Inf. Process. Syst., pages 593–601, 2012. 33332358