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2016 | OriginalPaper | Buchkapitel

Kernelized Subspace Ranking for Saliency Detection

verfasst von : Tiantian Wang, Lihe Zhang, Huchuan Lu, Chong Sun, Jinqing Qi

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a novel saliency method that takes advantage of object-level proposals and region-based convolutional neural network (R-CNN) features. We follow the learning-to-rank methodology, and solve a ranking problem satisfying the constraint that positive samples have higher scores than negative ones. As the dimensionality of the deep features is high and the amount of training data is low, ranking in the primal space is suboptimal. A new kernelized subspace ranking model is proposed by jointly learning a Rank-SVM classifier and a subspace projection. The projection aims to measure the pairwise distances in a low-dimensional space. For an image, the ranking score of each proposal is assigned by the learnt ranker. The final saliency map is generated by a weighted fusion of the top-ranked candidates. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on four benchmark datasets.

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Achanta, R., Hemami, S., Estrada, F., Susstrunk., S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)

Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE TPAMI 34(11), 2189–2202 (2012)CrossRef

Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)CrossRef

Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE TPAMI 34(7), 1312–1328 (2012)CrossRef

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)

Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Inf. Retrieval 13(3), 201–215 (2010)CrossRef

Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)

Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: Bing: binarized normed gradients for objectness estimation at 300fps. In: CVPR (2014)

Cheng, M., Warrell, J., Lin, W., Zheng, S., Vineet, V., Crook., N.: Efficient salient region detection with soft image abstraction. In: ICCV, pp. 1529–1536 (2013)

10.

Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu., S.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)

11.

Endres, I., Hoiem, D.: Category independent object proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 575–588. Springer, Heidelberg (2010)CrossRef

12.

Girshick, R., Donahue, J., Darrell, T., Malik, J.: Heterogeneous metric learning with joint graph regularization for cross-media retrieval. In: CVPR (2014)

13.

Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010)

14.

Gong, C., Tao, D., Liu, W., Maybank, S.J., Fang, M., Fu, K., Yang, J.: Saliency propagation from simple to difficult. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2531–2539 (2015)

15.

Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE TIP 19(12), 3232–3242 (2010)MathSciNet

16.

Guo, C., Ma, Q., Zhang., L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: CVPR (2008)

17.

Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)

18.

Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 297–312. Springer, Heidelberg (2014)

19.

Hou, X., Zhang., L.: Saliency detection: a spectral residual approach. In: CVPR, pp. 1–8 (2007)

20.

Huang, Z., Wang, R., Shan, S., Chen, X.: Learning Euclidean-to-Riemannian metric for point-to-set classification. In: CVPR, pp. 1677–1684 (2014)

21.

Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20(11), 1254–1259 (1998)CrossRef

22.

Jia, Y., Han., M.: Category-independent object-level saliency detection. In: ICCV, pp. 1761–1768 (2013)

23.

Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: ICCV, pp. 1665–1672 (2013)

24.

Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: CVPR (2013)

25.

Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: Uniqueness, Focusness and Objectness. In: ICCV, pp. 1976–1983 (2013)

26.

Jiang, P., Vasconcelos, N., Peng, J.: Generic promotion of diffusion-based salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 217–225 (2015)

27.

Jiang, Z., Davis, L.: Submodular salient region detection. In: CVPR, pp. 2043–2050 (2013)

28.

Judd, T., Ehinger, K., Durand, F., Torralba., A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113 (2009)

29.

Kienzle, W., Wichmann, F.A., Schölkopf, B., Franz, M.O.: A nonparametric approach to bottom-up visual saliency. In: NIPS, pp. 417–424 (2007)

30.

Kim, J., Han, D., Tai, Y.W., Kim, J.: Salient region detection via high-dimensional color transform. In: CVPR, pp. 883–890 (2014)

31.

Klein, D., Frintrop., S.: Center-surround divergence of feature statistics for salient object detection. In: ICCV, pp. 2214–2219 (2011)

32.

Krähenbühl, P., Koltun, V.: Geodesic object proposals. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 725–739. Springer, Heidelberg (2014)

33.

Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)

34.

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)

35.

Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: CVPR (2015)

36.

Li, S., Lu, H., Lin, Z., Shen, X., Price, B.: Adaptive metric learning for saliency detection. IEEE TIP 24(11), 3321–3331 (2015)MathSciNet

37.

Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 2976–2983 (2013)

38.

Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: CVPR, pp. 280–287 (2014)

39.

Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRef

40.

Lu, S., Mahadevan, V., Vasconcelos, N.: Learning optimal seeds for diffusion-based salient object detection. In: CVPR, pp. 2790–2797 (2014)

41.

Lu, Y., Zhang, W., Lu, H., Xue, X.: Salient object detection using concavity context. In: ICCV, pp. 233–240 (2011)

42.

Mai, L., Niu, Y., Liu, F.: Saliency aggregation: a data-driven approach. In: CVPR, pp. 1131–1138 (2013)

43.

Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: CVPR, pp. 1139–1146 (2013)

44.

Mcfee, B., Lanckriet, G., Jebara, T.: Learning multi-modal similarity. JMLR 12(2), 491–523 (2011)MATHMathSciNet

45.

Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2666–2672 (2012)

46.

Movahedi, V., Elder, J.: Design and perceptual validation of performance measures for salient object segmentation. In: CVPR Workshop, pp. 49–56 (2010)

47.

Peng, Y., Xiao, J.: Heterogeneous metric learning with joint graph regularization for cross-media retrieval. In: AAAI, pp. 1198–1204 (2013)

48.

Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung., A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)

49.

Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 110–119 (2015)

50.

Toet, A.: Computational versus psychophysical bottom-up image saliency: a comparative evaluation study. IEEE TPAMI 33(11), 2131–2146 (2011)CrossRef

51.

Tong, N., Lu, H., Ruan, X., Yang, M.H.: Salient object detection via bootstrap learning. In: CVPR, pp. 1884–1892 (2015)

52.

Wang, L., Lu, H., Ruan, X., Yang, M.H.: Deep networks for saliency detection via local estimation and global search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3183–3192 (2015)

53.

Wang, M., Konrad, J., Ishwar, P., Jing, K., Rowley, H.: Image saliency: from intrinsic to extrinsic context. In: CVPR, pp. 417–424 (2011)

54.

Wang, W., Wang, Y., Huang, Q., Gao., W.: Measuring visual saliency by site entropy rate. In: CVPR, pp. 2368–2375 (2010)

55.

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)CrossRef

56.

Xiong, F., Gou, M., Camps, O., Sznaier, M.: Person re-identification using kernel-based metric learning methods. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 1–16. Springer, Heidelberg (2014)

57.

Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162 (2013)

58.

Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013)

59.

Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 fps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1404–1412 (2015)

60.

Zhang, T., Oles, F.J.: Text categorization based on regularized linear classification methods. Inf. Retrieval 4(1), 5–31 (2001)CrossRefMATH

61.

Zhao, Q., Koch, C.: Learning a saliency map using fixated locations in natural scenes. JoV 11(3), 1–15 (2011)CrossRef

62.

Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)

63.

Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR, pp. 2814–2821 (2014)

64.

Zou, W., Komodakis, N.: HARF: hierarchy-associated rich features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 406–414 (2015)

Titel: Kernelized Subspace Ranking for Saliency Detection
verfasst von: Tiantian Wang
Lihe Zhang
Huchuan Lu
Chong Sun
Jinqing Qi
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46484-8_27

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