Saliency Detection via Objectness Transferring

  • Quan ZhouEmail author
  • Yawen Fan
  • Weihua Ou
  • Huimin Lu
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


In this paper, we present a novel framework to incorporate top-down guidance to identify salient objects. The salient regions/objects are predicted by transferring objectness prior without the requirement of center-biased assumption. The proposed framework consists of the following two basic steps: In the top-down process, we create a location saliency map (LSM), which can be identified by a set of overlapping windows likely to cover salient objects. The corresponding binary segmentation masks of training windows are treated as high-level knowledge to be transferred to the test image windows, which may share visual similarity with training windows. In the bottom-up process, a multi-layer segmentation framework is employed, providing local shape information that is used to delineate accurate object boundaries. Through integrating top-down objectness priors and bottom-up image representation, our approach is able to produce an accurate pixel-wise saliency map. Extensive experiments show that our approach achieves the state-of-the-art results over MSRA 1000 dataset.


Salient object detection Objectness priors Location saliency map Multi-layer segmentation 



This work was partly supported by the National Natural Science Foundation of China (Grant No. 61876093, 61881240048, 61571240, 61501247, 61501259, 61671253, 61762021), Natural Science Foundation of Jiangsu Province (Grant No. BK20181393, BK20150849, BK20160908), Huawei Innovation Research Program (HIRP2018), and Natural Science Foundation of Guizhou Province (Grant No. [2017]1130).


  1. 1.
    Borji, A., Tavakoli, H.R., Sihite, D.N., Itti, L.: Analysis of scores, datasets, and models in visual saliency prediction. In: CVPR, pp. 921–928 (2013)Google Scholar
  2. 2.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. TPAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  3. 3.
    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: CVPR, pp. 2083–2090 (2013)Google Scholar
  4. 4.
    Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV (2013)Google Scholar
  5. 5.
    Marchesotti, L., Cifarelli, C., Csurka, G.: A framework for visual saliency detection with applications to image thumbnailing. In: ICCV, pp. 2232–2239 (2009)Google Scholar
  6. 6.
    Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162 (2013)Google Scholar
  7. 7.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013)Google Scholar
  8. 8.
    Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: ICCV, pp. 73–80 (2013)Google Scholar
  9. 9.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR. pp. 73–80 (2010)Google Scholar
  10. 10.
    Gao, D., Han, S., Vasconcelos, N.: Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. TPAMI 31(6), 989–1005 (2009)CrossRefGoogle Scholar
  11. 11.
    Toshev, A., Shi, J., Daniilidis, K.: Image matching via saliency region correspondences. In: CVPR, pp. 1–8 (2007)Google Scholar
  12. 12.
    Jung, C., Kim, C.: A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. TIP 21(3), 1272–1283 (2012)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Mahadevan, V., Vasconcelos, N.: Saliency-based discriminant tracking. In: CVPR, pp. 1007–1013 (2009)Google Scholar
  14. 14.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010)Google Scholar
  15. 15.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. TPAMI 33(2), 353–367 (2011)CrossRefGoogle Scholar
  16. 16.
    Tatler, B.: The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. J. Vision 7(14), 1–17 (2007)CrossRefGoogle Scholar
  17. 17.
    Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, pp. 853–860 (2012)Google Scholar
  18. 18.
    Borji, A., Sihite, D.N., Itti, L.: Salient object detection: A benchmark. In: ECCV, pp. 414–429 (2012)Google Scholar
  19. 19.
    Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: ECCV (2012)Google Scholar
  20. 20.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113 (2009)Google Scholar
  21. 21.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. EPEL, Tech. Rep 149300 (2010)Google Scholar
  22. 22.
    Kuettel, D., Ferrari, V.: Figure-ground segmentation by transferring window masks. In: CVPR, pp. 558–565 (2012)Google Scholar
  23. 23.
    Achanta, R., Estrada, F., Wils, P., Susstrunk, S.: Salient region detection and segmentation. Comput. Vision Syst. 66–75 (2008)Google Scholar
  24. 24.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)Google Scholar
  25. 25.
    Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)Google Scholar
  26. 26.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR, pp. 1–8 (2007)Google Scholar
  27. 27.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)Google Scholar
  28. 28.
    Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: Contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)Google Scholar
  29. 29.
    Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACMMM, pp. 815–824 (2006)Google Scholar
  30. 30.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)CrossRefGoogle Scholar
  31. 31.
    Wang, J., Lu, H., Li, X., Tong, N., Liu, W.: Saliency detection via background and foreground seed selection. Neurocomputing 152, 359–368 (2015)CrossRefGoogle Scholar
  32. 32.
    Zhou, Q.: Object-based attention: saliency detection using contrast via background prototypes. EL 50(14), 997–999 (2014)Google Scholar
  33. 33.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. TPAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  34. 34.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888–905 (2000)CrossRefGoogle Scholar
  35. 35.
    Everingham, M., Zisserman, A., Williams, C.K.I., Van Gool, L.: The PASCAL visual object classes challenge 2006 (VOC2006) results.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Engineering Research Center of Communications and NetworkingNanjing University of Posts & TelecommunicationsNanjingPeople’s Republic of China
  2. 2.State Key Lab. for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  3. 3.School of Big Data and Computer ScienceGuizhou Normal UniversityGuiyangPeople’s Republic of China
  4. 4.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyKitakyushuJapan

Personalised recommendations