Advertisement

Saliency object detection: integrating reconstruction and prior

  • Cuiping Li
  • Zhenxue Chen
  • Q. M. Jonathan Wu
  • Chengyun Liu
Original paper
  • 22 Downloads

Abstract

To remedy some challenging cases in saliency detection such as complex background and multiple objects. A new saliency object detection approach is proposed via integrating reconstruction and prior knowledge. This paper first segments each image into super pixels using over-segmentation algorithm. Then, the reconstruction saliency map and prior saliency map are generated by reconstruction and prior, respectively. The reconstruction involves dense reconstruction and sparse reconstruction. When the saliency object appears on the image boundaries, the detection can be more accurate via dense reconstruction. In addition, if there is complex background in natural scene image, the sparse reconstruction can be more robust and suppress the background effectively. The prior adopts background prior and center prior, which can highlight the saliency object uniformly. The reconstruction saliency map and prior saliency map are nonlinearly integrated to generate the final saliency map. The proposed method is compared with the other five state-of-the-art algorithms based on comprehensive metrics. The experimental results demonstrate that the proposed algorithm has superior saliency detection performance and low average elapsing time.

Keywords

Saliency detection Over-segmentation Reconstruction Prior Integration 

Notes

Acknowledgements

This work has been supported by National Natural Science Foundation of China (61203261; 61876099), China Postdoctoral Science Foundation funded project (2012M521335), Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (MIMS16-02), Shenzhen Science and Technology Research (JCYJ20170307093018753), and Development Funds and The Fundamental Research Funds of Shandong University (2017JC043 and 2018JCG07).

References

  1. 1.
    Li, Q., Zhou, Y., Yang, J.: Saliency based image segmentation. In: ICMT, pp. 5068–5071 (2011)Google Scholar
  2. 2.
    Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition. In: CVPR (2004)Google Scholar
  3. 3.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE TIP 19(1), 185–198 (2010)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Frintrop, S., Kessel, M.: Most salient region tracking. In: IEEE ICRA, pp. 1869–1874 (2009)Google Scholar
  5. 5.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE PAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  6. 6.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency tuned salient region detection. In: CVPR (2009)Google Scholar
  7. 7.
    Cheng, M.-M., Zhang, G.-X., Mitra, N.J., Huang, X., Hu, S.-M.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)Google Scholar
  8. 8.
    Cheng, M.-M., Warrell, J., Lin, W.-Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: ICCV, pp. 1529–1536 (2013)Google Scholar
  9. 9.
    Xie, Y., Lu, H., Yang, M.-H.: Bayesian saliency via low and midlevel cues. IEEE TIP 22(5), 1689–1698 (2013)zbMATHGoogle Scholar
  10. 10.
    Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: CVPR (2008)Google Scholar
  11. 11.
    Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: ECCV, pp. 29–42 (2012)Google Scholar
  12. 12.
    Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR (2014)Google Scholar
  13. 13.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC super pixels. EPFL, Lausanne, Switzerland, Technical Report 149300 (2010)Google Scholar
  14. 14.
    Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.-H.: Saliency detection via dense and sparse reconstruction. In: ICCV (2013)Google Scholar
  15. 15.
    Huo, L., Yang, S., Jiao, L., Wang, S., Wang, S.: Local graph regularized sparse reconstruction for salient object detection. Neurocomputing 194, 348–359 (2016)CrossRefGoogle Scholar
  16. 16.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.-H.: Saliency detection via graph-based manifold ranking. In: CVPR (2013)Google Scholar
  17. 17.
    Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process. Lett. 20, 637–640 (2013)CrossRefGoogle Scholar
  18. 18.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1915–1925 (2012)CrossRefGoogle Scholar
  19. 19.
    Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: BMVC (2011)Google Scholar
  20. 20.
    Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR (2012)Google Scholar
  21. 21.
    Neverz, I., Lin, W., Fang, Y.: A saliency detection model using low level features based on wavelet transform. IEEE Trans. Multimed. 15(1), 96–105 (2012)Google Scholar
  22. 22.
    Yan, Q., Xu, L., Shi, J., et al.: Hierarchical saliency detection. computer vision and pattern recognition. In: IEEE, pp. 1155–1162 (2013)Google Scholar
  23. 23.
    Li, G., Yu, Y.: Visual saliency based on multiscale deep features. computer vision and pattern recognition. In: IEEE, pp. 5455–5463 (2015)Google Scholar
  24. 24.
    Alpert, S., Galun, M., Basri, R., et al.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Computer Vision and Pattern Recognition, IEEE, pp. 1–8 (2007)Google Scholar
  25. 25.
    Peng, H., Li, B., Ling, H., et al.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818–832 (2017)CrossRefGoogle Scholar
  26. 26.
    Yuan, Y., Li, C., Kim, J., Cai, W., et al.: Reversion correction and regularized random walks ranking for saliency detection. IEEE Trans. Image Process. 27(3), 1311–1322 (2018)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Yao, Q., Lu, H., Xu, Y., He, W.: Saliency detection via cellular automata. In: Computer Vision and Pattern Recognition, IEEE, pp. 110–119 (2015)Google Scholar
  28. 28.
    Kim, J., Han, D., Tai, Y.-W., Kim, J.: Salient region detection via high-dimensional color transform. In: Computer Vision and Pattern Recognition, IEEE (2014)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Control Science and EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.Department of Electrical and Computer EngineeringUniversity of WindsorWindsorCanada

Personalised recommendations