Skip to main content
SpringerLink
Log in

Saliency Detection via Combining Global Shape and Local Cue Estimation

  • Conference paper
  • First Online:
Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

  • 2312 Accesses

Abstract

Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency-detection algorithms.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Huang, K., et al.: Biologically inspired features for scene classification in video surveillance. IEEE Trans. Syst. Man Cybern. B Cybern. 41(1), 307–313 (2010)

    Article  Google Scholar 

  2. Zhang, K., Zhang, L., Yang, M.H.: Fast compressive tracking. IEEE Trans. PAMI 36(10), 2002–2015 (2014)

    Article  Google Scholar 

  3. Zhang, K., Liu, Q., Wu, Y.: Robust visual tracking via convolutional networks without training. IEEE TIP 25(4), 1779–1792 (2016)

    MathSciNet  Google Scholar 

  4. Jian, M.W., Dong, J.Y., Ma, J.: Image retrieval using wavelet-based salient regions. Imaging Sci. J. 59(4), 219–231 (2011)

    Article  Google Scholar 

  5. Zhu, G., Wang, Q., Yuan, Y., Yan, P.: Learning saliency by MRF and differential threshold. IEEE Trans. Cybern. 43(6), 2032–2043 (2013)

    Article  Google Scholar 

  6. Hsu, C.Y., Ding, J.J.: Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging. In: Information, Communications and Signal Processing, pp. 1–5 (2013)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  9. Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: ACM Conference on Multimedia, pp. 374–381 (2003)

    Google Scholar 

  10. Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Proceedings of 11th ECCV, pp. 366–379 (2010)

    Google Scholar 

  11. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. PAMI 34(10), 1915–1926 (2012)

    Article  Google Scholar 

  12. Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study. IEEE TIP 22(1), 55–69 (2013)

    MathSciNet  Google Scholar 

  13. Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 11 (2013)

    Article  Google Scholar 

  14. Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. IEEE Sig. Process. Lett. 20(7), 637–640 (2013)

    Article  Google Scholar 

  15. Tong, N., Lu, H., Zhang, L., et al.: Saliency detection with multi-scale superpixels. IEEE Sig. Process. Lett. 21(9), 1035–1039 (2014)

    Article  Google Scholar 

  16. Cheng, M.M., Mitra, N.J., Huang, X., et al.: Global contrast based salient region detection. IEEE Trans. PAMI 37(3), 569–582 (2015)

    Article  Google Scholar 

  17. Qin, Y., Lu, H., Xu, Y., et al.: Saliency detection via cellular automata. In: IEEE CVPR, pp. 110–119 (2015)

    Google Scholar 

  18. Jian, M., Lam, K.M., Dong, J.J., Shen, L.L.: Visual-patch-attention-aware saliency detection. IEEE T. Cybernetics 45(8), 1575–1586 (2015)

    Article  Google Scholar 

  19. Jian, M.M., Qi, Q., Sun, Y., Lam, K.M., et al: Saliency detection using quaternionic distance based weber descriptor and object cues. In: APSIPA ASC 2016, Korean (2016)

    Google Scholar 

  20. Zhang, D., Meng, D., Han, J.: Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans. Pattern Anal. Mach. Intell. (2016)

    Google Scholar 

  21. Wang, A., Wang, M.: RGB-D salient object detection via minimum barrier distance transform and saliency fusion. IEEE Sig. Process. Lett. 24(5), 663–667 (2017)

    Article  Google Scholar 

  22. Lu, H., Zhang, X., Qi, J., et al.: Co-Bootstrapping saliency. IEEE Trans. Image Process. 26(1), 414–425 (2017)

    Article  MathSciNet  Google Scholar 

  23. Lin, X., Yan, Z., Jiang, L.: Saliency detection via foreground and background seeds. In: Kim, K., Joukov, N. (eds.) ICISA 2017. LNEE, vol. 424, pp. 145–154. Springer, Singapore (2017). doi:10.1007/978-981-10-4154-9_18

    Chapter  Google Scholar 

  24. Oliva, A., Torralba, A., Castelhano, M.S., et al.: Top-down control of visual attention in object detection. In: IEEE ICIP, vol. 1 (2003)

    Google Scholar 

  25. Cholakkal, H., Rajan, D., Johnson, J.: Top-down saliency with locality-constrained contextual sparse coding. In: BMVC (2015)

    Google Scholar 

  26. Yang, J., Yang, M.H.: Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans. PAMI 39(3), 576–588 (2017)

    Article  Google Scholar 

  27. He, S., Lau, R.W.H., Yang, Q.: Exemplar-driven top-down saliency detection via deep association. In: IEEE CVPR, pp. 5723–5732 (2016)

    Google Scholar 

  28. Rahman, I., Hollitt, C., Zhang, M.: Contextual-based top-down saliency feature weighting for target detection. Mach. Vis. Appl. 27(6), 893–914 (2016)

    Article  Google Scholar 

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

    Google Scholar 

  30. Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE CVPR, pp. 733–740 (2012)

    Google Scholar 

  31. Lan, R.S., Zhou, Y.C., Tang, Y.: Quaternionic weber local descriptor of color images. IEEE Trans. Circuits Syst. Video Technol. (2015)

    Google Scholar 

  32. Tong, N., Lu, H., Zhang, Y., et al.: Salient object detection via global and local cues. Pattern Recogn. 48(10), 3258–3267 (2015)

    Article  Google Scholar 

  33. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE CVPR, pp. 3360–3367 (2010)

    Google Scholar 

  34. Cheng, M.M., Mitra, N.J., Huang, X.: Salient shape: group saliency in image collections. Vis. Comput. 30(4), 443–453 (2014)

    Article  Google Scholar 

  35. Jian, M., Qi, Q., et al.: Saliency detection using quaternionic distance based weber local descriptor and level priors. Multimedia Tools Appl. (2017). doi:10.1007/s11042-017-5032-z

    Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (NSFC) (61601427); Natural Science Foundation of Shandong Province (ZR2015FQ011); Applied Basic Research Project of Qingdao (16-5-1-4-jch); China Postdoctoral Science Foundation funded project (2016M590659); Postdoctoral Science Foundation of Shandong Province (201603045); Qingdao Postdoctoral Science Foundation funded project (861605040008) and The Fundamental Research Funds for the Central Universities (201511008, 30020084851).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Qiang Qi, Muwei Jian & Yilong Yin

  2. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

    Qiang Qi, Muwei Jian & Junyu Dong

  3. School of Information Science and Engineering, Linyi University, Linyi, China

    Wenyin Zhang

  4. School of Creative Technologies, University of Portsmouth, Portsmouth, UK

    Hui Yu

Authors
  1. Qiang Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muwei Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yilong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junyu Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenyin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hui Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muwei Jian .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Qi, Q., Jian, M., Yin, Y., Dong, J., Zhang, W., Yu, H. (2017). Saliency Detection via Combining Global Shape and Local Cue Estimation. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67777-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation