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Deep Neural Network Based Salient Object Detection with Image Enhancement

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11304))

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Abstract

Salient object detection aims to discover the most visually attractive regions from images. It allows more efficient follow-up processing of images without handling redundant information. In this paper, we propose a novel framework based on deep neural network to detect salient objects. The proposed framework introduces feature enhancement to input images to improve the performance of the fully convolutional neural network (FCN). Images are segmented and weighted through superpixel based pulse coupled neural networks. Low-level features including contrast and spatial features are extracted during this procedure by removing background disturbance in images. Subsequent neural network takes the enhanced images in and produces the saliency maps. Finally, some refinements are made afterwards to achieve better saliency results. Experimental results on five representative benchmarks show the superiority of our model than other state-of-the-art methods. Furthermore, comparisons are made to verify the effectiveness of image enhancement part in our model.

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References

  1. 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)

    Article  Google Scholar 

  2. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 22th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604. IEEE Press, Miami (2009)

    Google Scholar 

  3. Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 409–416 (2011)

    Google Scholar 

  4. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173. IEEE Press, Portland (2013)

    Google Scholar 

  5. Li, X., Lu, H., Zhang, L., Xiang, R., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2976–2983. IEEE Press, Portland (2013)

    Google Scholar 

  6. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: 14th IEEE International Conference on Computer Vision, pp. 1665–1672. IEEE Press, Sydney (2013)

    Google Scholar 

  7. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090. IEEE Press, Portland (2013)

    Google Scholar 

  8. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162. IEEE Press, Portland (2013)

    Google Scholar 

  9. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821. IEEE Press, Columbus (2014)

    Google Scholar 

  10. Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: 14th IEEE International Conference on Computer Vision, pp. 1529–1536. IEEE Press, Sydney (2013)

    Google Scholar 

  11. Chen, T., Lin, L., Liu, L., Luo, X., Li, X.: DISC: deep image saliency computing via progressive representation learning. IEEE Trans. Neural Networks Learn. Syst. 27(6), 1135–1149 (2016)

    Article  MathSciNet  Google Scholar 

  12. Li, X., Zhao, L., Wei, L., Yang, M.H.: DeepSaliency: multi-task deep neural network model for salient object detection. IEEE Trans. Image Process. 25(8), 3919–3930 (2016)

    Article  MathSciNet  Google Scholar 

  13. He, S., Lau, R.H.W., Liu, W., Huang, Z., Yang, Q.: SuperCNN: a superpixelwise convolutional neural network for salient object detection. Int. J. Comput. Vision 115(3), 330–344 (2015)

    Article  MathSciNet  Google Scholar 

  14. Li, H., Chen, J., Lu, H., Chi, Z.: CNN for saliency detection with low-level feature integration. Neurocomputing 226(C), 212–220 (2017)

    Article  Google Scholar 

  15. Hou, Q., Cheng M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.S.: Deeply supervised salient object detection with short connections. In: 30th IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212. IEEE Press, Honolulu (2017)

    Google Scholar 

  16. Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput. 2(3), 293–307 (1990)

    Article  Google Scholar 

  17. Gu, X.: Feature extraction using unit-linking pulse coupled neural network and its applications. Neural Process. Lett. 27(1), 25–41 (2008)

    Article  Google Scholar 

  18. Wei, W., Li, Z.: Automated image segmentation based on modified PCNN and mutual information entropy. Comput. Eng. 36(13), 199 (2010)

    Google Scholar 

  19. Anchanta, R., Shaji, A., Smith, K., Luchhi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  20. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 28th IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE Press, Boston (2015)

    Google Scholar 

  21. Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: 20th IEEE Conference on Computer Vision and Pattern Recognition, pp. 353–367. IEEE Press, Minneapolis (2007)

    Google Scholar 

  22. Cheng, M.M., Mitra, N.J., Huang, X., Hu, S.M.: SalientShape: group saliency in image collections. Visual Comput. Int. J. Comput. Graphics 30(4), 443–453 (2014)

    Google Scholar 

  23. Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 315–327 (2012)

    Article  Google Scholar 

  24. Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287. IEEE Press, Columbus (2014)

    Google Scholar 

  25. Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: 22nd ACM International Conference on Multimedia, pp. 675–678. ACM Press, Orlando (2014)

    Google Scholar 

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under grant 61771145 and 61371148.

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Authors and Affiliations

  1. Department of Electronic Engineering, Fudan University, Shanghai, 200433, China

    Lecheng Zhou & Xiaodong Gu

Authors
  1. Lecheng Zhou
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  2. Xiaodong Gu
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Corresponding author

Correspondence to Xiaodong Gu .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Zhou, L., Gu, X. (2018). Deep Neural Network Based Salient Object Detection with Image Enhancement. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_39

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  • DOI: https://doi.org/10.1007/978-3-030-04212-7_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04211-0

  • Online ISBN: 978-3-030-04212-7

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