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Extended Non-local Feature for Visual Saliency Detection in Low Contrast Images

  • Xin XuEmail author
  • Jie Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Saliency detection model can substantially facilitate a wide range of applications. Conventional saliency detection models primarily rely on high level features from deep learning and hand-crafted low-level image features. However, they may face great challenges in nighttime scenario, due to the lack of well-defined feature to represent saliency information in low contrast images. This paper proposes a saliency detection model for nighttime scene. This model is capable of extracting non-local feature that is jointly learned with local features under a unified deep learning framework. The key idea of the proposed model is to hierarchically introduce non-local module with local contrast processing blocks, aiming to provide robust representation of saliency information towards low contrast images with low signal-to-noise ratio property. Besides, both nighttime and daytime images are utilized in training to provide complementary information. Extensive experiments have been conducted on five challenging datasets and our nighttime image dataset to evaluate the performance of the proposed model.

Keywords

Deep learning Non-local feature Saliency detection Low contrast images 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of China (61602349 and 61440016).

References

  1. 1.
    Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5455–5463 (2015)Google Scholar
  2. 2.
    Chen, T., Cheng, M.-M., Tan, P., Shamir, A., Hu, S.-M.: Sketch2photo: internet image montage. ACM Trans. Graph. 28(5), 124:1–124:10 (2009)Google Scholar
  3. 3.
    Hu, S.-M., Chen, T., Xu, K., Cheng, M.-M., Martin, R.-R.: Internet visual media processing: a survey with graphics and vision applications. Vis. Comput. 29(5), 393–405 (2013)CrossRefGoogle Scholar
  4. 4.
    Borji, A., Frintrop, S., Sihite, D.-N., Itti, L.: Adaptive object tracking by learning background context. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 23–30 (2012)Google Scholar
  5. 5.
    Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cheng, M.-M., Hou, Q.-B., Zhang, S.-H., Rosin, P.-L.: Intelligent visual media processing: when graphics meets vision. J. Comput. Sci. Technol. 32(1), 110–121 (2017)CrossRefGoogle Scholar
  7. 7.
    Mehrani, P., Veksler, O.: Saliency segmentation based on learning and graph cut refinement. In: British Machine Vision Conference, pp. 110.1–110.12 (2010)Google Scholar
  8. 8.
    Zhang, J., Wang, M., Zhang, S., Li, X., Wu, X.: Spatiochromatic context modeling for color saliency analysis. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1177–1189 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)CrossRefGoogle Scholar
  10. 10.
    Liu, Q., Hong, X., Zou, B., Chen, J., Chen, Z., Zhao, G.: Hierarchical contour closure-based holistic salient object detection. IEEE Trans. Image Process. 26(9), 4537–4552 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mu, N., Xu, X., Zhang, X., Zhang, H.: Salient object detection using a covariance-based CNN model in low-contrast images. Neural Comput. Appl. 29(8), 181–192 (2018)CrossRefGoogle Scholar
  12. 12.
    Pan, J., Sayrol, E., Giro-I-Nieto, X., McGuinness, K., O’Connor, N.-E.: Shallow and deep convolutional networks for saliency prediction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 598–606 (2016)Google Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–14 (2014)Google Scholar
  14. 14.
    Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 478–487 (2016)Google Scholar
  15. 15.
    Liu, N., Han, J., Zhang, D., Wen, S., Liu, T.: Predicting eye fixations using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 362–370 (2015)Google Scholar
  16. 16.
    Li, X., et al.: DeepSaliency: multi-task deep neural network model for salient object detection. IEEE Trans. Image Process. 25(8), 3919–3930 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)Google Scholar
  18. 18.
    Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., Jodoin, P.-M.: Non-local deep features for salient object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6593–6601 (2017)Google Scholar
  19. 19.
    Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5300–5309 (2017)Google Scholar
  20. 20.
    Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: uniqueness, focusness and objectness. In: IEEE International Conference on Computer Vision, pp. 1976–1983 (2013)Google Scholar
  21. 21.
    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. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33712-3_3CrossRefGoogle Scholar
  22. 22.
    Chang, K.-Y., Liu, T.-L., Chen, H.-T., Lai, S.-H.: Fusing generic objectness and visual saliency for salient object detection. In: IEEE International Conference on Computer Vision, pp. 914–921 (2011)Google Scholar
  23. 23.
    Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE International Conference on Computer Vision, pp. 733–740 (2012)Google Scholar
  24. 24.
    Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)Google Scholar
  25. 25.
    Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60–65 (2005)Google Scholar
  26. 26.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision, pp. 839–846 (1998)Google Scholar
  27. 27.
    Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  28. 28.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.: Saliency detection via graph-based manifold ranking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)Google Scholar
  29. 29.
    Li, Y., Hou, X., Koch, C., Rehg, J., Yuille, A.: The secrets of salient object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR, pp. 280–287 (2014)Google Scholar
  30. 30.
    Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)Google Scholar
  31. 31.
    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)Google Scholar
  32. 32.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations, pp. 1–15 (2014)Google Scholar
  33. 33.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)Google Scholar
  34. 34.
    Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–740 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhan University of Science and TechnologyWuhanChina

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