Skip to main content
SpringerLink
Log in

Saliency Detection via Objectness Transferring

  • Chapter
  • First Online:
Book cover Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

Included in the following conference series:

  • 781 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. TPAMI 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  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. Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV (2013)

    Google Scholar 

  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. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162 (2013)

    Google Scholar 

  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. Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: ICCV, pp. 73–80 (2013)

    Google Scholar 

  9. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR. pp. 73–80 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  11. Toshev, A., Shi, J., Daniilidis, K.: Image matching via saliency region correspondences. In: CVPR, pp. 1–8 (2007)

    Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  13. Mahadevan, V., Vasconcelos, N.: Saliency-based discriminant tracking. In: CVPR, pp. 1007–1013 (2009)

    Google Scholar 

  14. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Borji, A., Sihite, D.N., Itti, L.: Salient object detection: A benchmark. In: ECCV, pp. 414–429 (2012)

    Google Scholar 

  19. Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: ECCV (2012)

    Google Scholar 

  20. Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113 (2009)

    Google Scholar 

  21. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. EPEL, Tech. Rep 149300 (2010)

    Google Scholar 

  22. Kuettel, D., Ferrari, V.: Figure-ground segmentation by transferring window masks. In: CVPR, pp. 558–565 (2012)

    Google Scholar 

  23. Achanta, R., Estrada, F., Wils, P., Susstrunk, S.: Salient region detection and segmentation. Comput. Vision Syst. 66–75 (2008)

    Google Scholar 

  24. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)

    Google Scholar 

  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. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR, pp. 1–8 (2007)

    Google Scholar 

  27. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)

    Google Scholar 

  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. Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACMMM, pp. 815–824 (2006)

    Google Scholar 

  30. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)

    Article  Google Scholar 

  31. Wang, J., Lu, H., Li, X., Tong, N., Liu, W.: Saliency detection via background and foreground seed selection. Neurocomputing 152, 359–368 (2015)

    Article  Google Scholar 

  32. Zhou, Q.: Object-based attention: saliency detection using contrast via background prototypes. EL 50(14), 997–999 (2014)

    Google Scholar 

  33. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. TPAMI 24(5), 603–619 (2002)

    Article  Google Scholar 

  34. Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888–905 (2000)

    Article  Google Scholar 

  35. Everingham, M., Zisserman, A., Williams, C.K.I., Van Gool, L.: The PASCAL visual object classes challenge 2006 (VOC2006) results. http://www.pascal-network.org/challenges/VOC/voc2006/results.pdf

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. National Engineering Research Center of Communications and Networking, Nanjing University of Posts & Telecommunications, Nanjing, People’s Republic of China

    Quan Zhou & Yawen Fan

  2. State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China

    Quan Zhou

  3. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, People’s Republic of China

    Weihua Ou

  4. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

Authors
  1. Quan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yawen Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weihua Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quan Zhou .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhou, Q., Fan, Y., Ou, W., Lu, H. (2020). Saliency Detection via Objectness Transferring. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_20

Download citation

Publish with us

Policies and ethics

Search

Navigation