Skip to main content
SpringerLink
Log in

Salient object detection via compactness and objectness cues

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Existing saliency detection algorithms are mainly patch-based. In this paper, we propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues. First, we obtain the objectness saliency map with objectness algorithm to find potential object candidates without need of category information. Second, the compactness map is generated by measuring color spatial distribution, and then it is refined by eliminating regions connecting to the selected boundary. Finally, to enforce the consistency among salient regions, we adopt graph-based manifold ranking algorithm by constructing two graphs each using a regional property descriptor. Both qualitative and quantitative evaluations on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 23 state-of-the-art methods in terms of six performance criterions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. 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)

    MathSciNet  MATH  Google Scholar 

  2. Wang, P., Wang, J., Zeng, G., Feng, J., Zha, H., Li, S.: Salient object detection for searched web images via global saliency. In: CVPR, pp. 3194–3201 (2012)

  3. Cheng, M., Mitra, N.J., Huang, X., Hu, S.: Salientshape: group saliency in image collections. Vis. Comput. 30(4), 443–453 (2014)

    Article  Google Scholar 

  4. Sun, J., Ling, H.: Scale and object aware image thumbnailing. IJCV 104(2), 135–153 (2013)

    Article  MathSciNet  Google Scholar 

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

  6. Margolin, R., Zelnik-Manor, L., Tal, A.: Saliency for image manipulation. TVC 29(5), 381–392 (2013)

    Article  Google Scholar 

  7. Li, J., Levine, M.D., An, X., Xu, X., He, H.: Visual saliency based on scale space analysis in the frequency domain. IEEE TPAMI 35(4), 996–1010 (2013)

    Article  Google Scholar 

  8. Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE TIP 24(12), 414–429 (2015)

    MathSciNet  Google Scholar 

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

  10. Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. JOV 9(12), 1–27 (2009)

    Article  Google Scholar 

  11. Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient object detection: a survey. Eprint Arxiv 16(7), 3118 (2014)

    Google Scholar 

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

    Article  Google Scholar 

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

  14. Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. IEEE TPAMI 33(2), 353–367 (2011)

    Article  Google Scholar 

  15. Achanta, R., Estrada, F., Wils, P., Susstrunk, S.: Salient region detection and segmentation. In: ICVS, pp. 66–75 (2008)

  16. Rahtu, E., Kannala, J., Salo, M., Heikkila, J.: Segmenting salient objects from images and videos. In ECCV, pp. 366–379 (2010)

  17. Achanta, R., Susstrunk, S.: Saliency detection using maximum symmetric surround. In: ICIP, pp. 2653–2656 (2010)

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

    Google Scholar 

  19. Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct. In: CVPR, pp. 2083–2090 (2013)

  20. Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: ICCV, pp. 2214–2219 (2011)

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

  22. Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.: Global contrast based salient region detection. IEEE TPAMI 37(3), 569–582 (2015)

    Article  Google Scholar 

  23. 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)

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

  25. Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng N.: Automatic salient object segmentation based on context and shape prior. In: BMVC (2011)

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

  27. Qi, W., Cheng, M., Borji, A., Lu, H., Bai, L.: Saliencyrank: two-stage manifold ranking for salient object detection. Comput. Vis. Media 1(4), 309–320 (2016)

    Article  Google Scholar 

  28. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminant regional feature integration approach. In: CVPR, pp. 2083–2090 (2013)

  29. Xie, Y., Lu, H., Yang, M.: Bayesian saliency via low and mid level cues. IEEE TIP 22(5), 1689–1698 (2013)

    MathSciNet  MATH  Google Scholar 

  30. Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 1665–1672 (2013)

  31. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: ICCV, pp. 2976–2983 (2013)

  32. Li, C., Yuan, Y., Cai, W., Xia, Y., Feng, D.D.: Robust saliency detection via regularized random walks ranking. In: IEEE CVPR, pp. 2710–2717 (2015)

  33. Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: ECCV, pp. 29–42 (2012)

  34. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173 (2013)

  35. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR, pp. 2814–2821 (2014)

  36. Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by ufo: Uniqueness, focusness and objectness. In: ICCV, pp. 1976–1983 (2013)

  37. Li, N., Ye, J., Ji, Y., Ling, H., Yu, J.: Saliency detection on light field. In: CVPR, pp. 2806–2813 (2014)

  38. Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE TPAMI 34(11), 2189–2202 (2012)

    Article  Google Scholar 

  39. Chang, K., Liu, T., Chen, H., Lai, S.: Fusing generic objectness and visual saliency for salient object detection. IEEE ICCV, pp. 914–921 (2011)

  40. Jia, Y., Han, M.: Category-independent object-level saliency detection. In: ICCV, pp. 1761–1768 (2013)

  41. Vikram, T.N., Tscherepanow, M., Wrede, B.: A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recognit. 45(9), 3114–3124 (2012)

    Article  Google Scholar 

  42. Li, H., Lu, H., Lin, Z., Shen, X., Price, B.: Inner and inter label propagation salient object detection in the wild. IEEE TIP 24(10), 3176–3186 (2015)

    MathSciNet  Google Scholar 

  43. Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, pp. 853–860 (2012)

  44. Tong, N., Lu, H., Ruan, X., Yang, M.-H.: Salient object detection via bootstrap learning. In: IEEE CVPR, pp. 1884–1892 (2015)

  45. Achanta, R., Shaji, A., Smith, S., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  46. Zhou, D., Weston, J., Gretton, A., Bousquet, O., Scholkopf B.: Ranking on data manifolds. In: NIPS (2004)

  47. Wang, J., Lu, H., Li, X., Tong, N., Liu, W.: Saliency detection via background and foreground seed selection. Neurocomputing 152(25), 359–368 (2015)

    Article  Google Scholar 

  48. Rosenfeld, A., Weinshall, D.: Extracting foreground masks towards object recognition. In: ICCV, pp. 1371–1378 (2011)

  49. Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps. In: CVPR, pp. 248–255 (2014)

  50. Maksai, A., Wang, X., Fua, P.: What players do with the ball: a physically constrained interaction modeling. In: CVPR, pp. 972–981 (2016)

  51. Wang, X., Turetken, E., Fleuret, F., Fua, P.: Tracking interacting objects using intertwined flows. IEEE TPAMI 38(11), 2312–2326 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61401281 and No.41671402 and Science Foundation of Shanghai under Grant No. 14ZR1440700.

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Qing Zhang, Wenju Li, Yanjiao Shi & Guogang Cao

  2. East China University of Science and Technology, Shanghai, 200237, China

    Jiajun Lin

Authors
  1. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiajun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenju Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanjiao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guogang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Lin, J., Li, W. et al. Salient object detection via compactness and objectness cues. Vis Comput 34, 473–489 (2018). https://doi.org/10.1007/s00371-017-1354-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-017-1354-0

Keywords

Search

Navigation