Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5329–5343 | Cite as

Image saliency detection for multiple objects

  • Beilei Wang
  • Lu MengEmail author
  • Jie Song


Traditional saliency detection methods are designed only for a single salient object and cannot detect multiple salient objects in the image. This paper proposes a novel method for detecting multiple salient objects in the image, which is based on both objectness estimation method and superpixel segmentation method. The present study shows that the proposed method can correctly detect the salient regions for multiple objects and outperforms the other three state-of-the-art saliency detection methods.


BING Multiple objects Saliency detection Super-pixel segmentation 



This research is supported by the National Natural Science Foundation of China (61662057, 61672143, U1435216), the Fundamental Research Funds for the Central Universities (N130404027, N151704004, N161602003), and Doctor Research Starting Foundation of Liaoning (No.20141011).


  1. 1.
    Achanta R, Susstrunk S (2010) Saliency detection using maximum symmetric surround. IEEE Int Conf Image Process 119(5):2653–2656Google Scholar
  2. 2.
    Achanta R, Shaji A, Smith K (2012) SLIC superpixels compared to State-of-the-Art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2281CrossRefGoogle Scholar
  3. 3.
    Alexe B, Deselaers T, Ferrari V (2010) What is an object. Comput Vis Pattern Recognit 23(3):73–80Google Scholar
  4. 4.
    Borji A, Cheng M-M, Hou Q, Jiang H, Li J (2014) Salient object detection: a survey. Eprint Arxiv 16(7):3118Google Scholar
  5. 5.
    Bruce N, Tsotsos JK (2005) Saliency based on information maximization. Int Conf Neural Inf Proces Syst 18(3):155–162Google Scholar
  6. 6.
    Cheng MM, Zhang ZM, Lin WY, Torr P (2014) BING: binarized normed gradients for objectness estimation at 300fps. 2017 I.E. conference on computer vision and pattern recognition, CVPR 2017. pp 3286–3293Google Scholar
  7. 7.
    Cheng MM, Zhang GX, Mitra NJ, Huang X, Hu SM (2015) J. Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582CrossRefGoogle Scholar
  8. 8.
    Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The PASCAL visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  9. 9.
    Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2015) The PASCAL visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136CrossRefGoogle Scholar
  10. 10.
    Huaizu JJ, Jingdong W, Zejian Y, Yang W, Nanning Z, Shipeng L(2013) Salient object detection: a discriminative regional feature integration approach. Comput Vis Pattern Recognit 123(2):2083–2090Google Scholar
  11. 11.
    Klein D, Frintrop S (2011) Center-surround divergence of features tatistics for salient object detection. IEEE Int Conf Comput Vis 50(2):2214–2219Google Scholar
  12. 12.
    Kootstra G, de Boer B, Schomaker L (2011) Predicting eyes on complex visual stimuli using local symmetry. Cogn Comput 3(1):223–240CrossRefGoogle Scholar
  13. 13.
    Oh K, Lee M, Kim G, Kim S (2016) Detection of multiple salient objects through the integration of estimated foreground clues. Image Vis Comput 54:31–44CrossRefGoogle Scholar
  14. 14.
    Oh K-h, Kim S-H, Kim Y-C, Lee Y-R (2016) Detection of multiple salient objects by categorizing regional features. KSII Trans Internet and Inf Syst 10(1):272–287Google Scholar
  15. 15.
    Rahtu E, Kannala J, Salo M (2010) Segmenting salient objects from images and videos. Eur Conf Comput Vis 6315:366–379Google Scholar
  16. 16.
    Rosin PL (2009) A simplemethod for detecting salient regions. J Pattern Recognit 42(11):2363–2371CrossRefGoogle Scholar
  17. 17.
    Rother C, Kolmogorov V, Blake A (2004) "GrabCut" - Interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314CrossRefGoogle Scholar
  18. 18.
    Shi J, Yan Q, Xu L, Jia J (2014) Hierarchical image saliency detection on extended CSSD. IEEE Trans Pattern Anal Mach Intell 38(4):717–729CrossRefGoogle Scholar
  19. 19.
    Yongdong Z, Zhendong M, Jintao L, Qi T (2014) Salient region detection for complex background images using integrated features. J Inf Sci 281:586–600CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of SoftwareNortheastern UniversityShenyangChina
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina

Personalised recommendations