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2017 | OriginalPaper | Buchkapitel

Vision Saliency Feature Extraction Based on Multi-scale Tensor Region Covariance

verfasst von : Shimin Wang, Mingwen Wang, Jihua Ye, Anquan Jie

Erschienen in: Information Retrieval

Verlag: Springer International Publishing

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Abstract

In the process of extracting image saliency features by using regional covariance, the low-level higher-order data are dealt with by vectorization, however, the structure of the data (color, intensity, direction) may be lost in the process, leading to a poorer representation and overall performance degradation. In this paper we introduce an approach for sparse representation of region covariance that will preserve the inherent structure of the image. This approach firstly calculates the image low-level data (color, intensity, direction), and then uses multi-scale transform to extract the multi-scale features for constructing tensor space, at last by using tensor sparse coding the image bottom features are extracted from region covariance. In the paper, it compares the experimental results with the commonly used feature extraction algorithms’ results. The experimental results show that the proposed algorithm is closer to the actual boundary of the object and achieving better results.

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Literatur
1.
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp. 1521–1528, June 2011
2.
Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 414–429 (2012)
3.
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRef
4.
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)
5.
Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognit. Psychol. 12(1), 97–136 (1980)CrossRef
6.
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 542–552 (2006)
7.
Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRef
8.
Bruce, N.D.B., Tsotsos, J.K.: Saliency based on information maximization. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 155–162 (2005)
9.
Hou, X., Zhang, L.: Dynamic attention: searching for coding length increments. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 681–688 (2008)
10.
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), 1–20 (2008)CrossRef
11.
Kong, L., Duan, L., Yang, W., Dou, Y.: Salient region detection: an integration approach based on image pyramid and region property. IET Comput. Vis. 9(1), 85–97 (2015)CrossRef
12.
Fang, Y., Lin, W., Chen, Z., Tsai, C.-M., Lin, C.-W.: A video saliency detection model in compressed domain. IEEE Trans. Circuits Syst. Video Technol. 24(1), 27–38 (2014)CrossRef
13.
Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12 (2011)
14.
Cerf, M., Frady, E.P., Koch, C.: Faces and text attract gaze independent of the task: experimental data and computer model. J. Vis. 9(12), 1–15 (2009)CrossRef
15.
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 795–825, September/October 2009
16.
Chang, K.-Y., Liu, T.-L., Chen, H.-T., Lai, S.-H.: Fusing generic abjectness and visual saliency for salient object detection. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 914–921, November 2011
17.
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 73–80, June 2010
18.
Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006). doi:10.​1007/​11744047_​45 CrossRef
19.
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1160–1169 (1985)CrossRef
20.
Wang, S., Cheng, B., Ye, J., Wang, M.: Method of face image feature extract based on weighted multi-scale tensor subspace. J. Data Acquis. Process. 31(04), 791–7 98 (2016)
21.
Sivalingam, R., Boley, D., Morellas, V., Papanikolopoulos, N.: Tensor sparse coding for region covariances. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 722–735. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-15561-1_​52 CrossRef
22.
Borji, A., Itti, L.: CAT2000: a large scale fixation dataset for boosting saliency research. In: CVPR 2015 Workshop on “Future of Datasets” (2015). arXiv preprint arXiv:​1505.​03581
23.
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., Durand, F.: What do different evaluation metrics tell us about saliency models? arXiv preprint arXiv:​1604.​03605 (2016)
24.
Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict Hum. Fixat. (2012)
25.
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)CrossRef
26.
Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 103–104 (2013)CrossRef
27.
Fang, S., Li, J., Tian, Y., et al.: Learning discriminative subspaces on random contrasts for image saliency analysis. IEEE Trans. Neural Netw. Learn. Syst. (2016)
28.
Riche, N., Mancas, M., Duvinage, M., et al.: RARE2012: a multi-scale rarity-based saliency detection with its comparative statistical analysis. Sig. Process. Image Commun. 28(6), 642–658 (2013)CrossRef
29.
Murray, N., Vanrell, M., Otazu, X., et al.: Saliency estimation using a non-parametric low-level vision model. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, 20–25 June, DBLP, pp. 433–440 (2011)
30.
Ran, M., Zelnik-Manor, L., Tal, A.: Saliency for image manipulation. Vis. Comput. 29(5), 381–392 (2013)CrossRef
Metadaten
Titel
Vision Saliency Feature Extraction Based on Multi-scale Tensor Region Covariance
verfasst von
Shimin Wang
Mingwen Wang
Jihua Ye
Anquan Jie
Copyright-Jahr
2017
Buch
Information Retrieval

Print ISBN: 978-3-319-68698-1

Electronic ISBN: 978-3-319-68699-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68699-8_15

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