Journal of Mathematical Imaging and Vision

, Volume 61, Issue 7, pp 990–1006 | Cite as

A Novel Probabilistic Contrast-Based Complex Salient Object Detection

  • Surya Kant SinghEmail author
  • Rajeev Srivastava


Saliency computation has wide applications. It is now being used in almost all vision-related applications. But, identifying saliency is still a problem. Various computational models have been proposed for identifying saliency. Global contrast-based method is used extensively. This method computes the contrast by measuring the color difference between image and specified region. It produces saliency with non-salient points, but, in the process, it loses some structural and spatial information. To address these limitations, the proposed method, i.e., Poisson-based probabilistic contrast, produces saliency with the concave topographical surface. This surface encloses the prominent object with all its structural and spatial information, or with all the salient features. Then, it is used as a reference plane for regional depth, color and spatial saliency integration. The proposed method has three stages. In the first stage, a probabilistic contrast is computed using Poisson-based maximum likelihood estimation by addition of chrominance and luminance contrast. The luminance contrast is normalized by proposed “enhance and suppress luminance method.” In the second stage, the regional color, depth, and spatial saliencies are integrated into the topographical surface to enhance the saliency. In the third and last stages, i.e., saliency enhancement stage, central saliency is used on global color distinction. The proposed method is evaluated on the publicly available datasets. Their performance is compared with 12 state-of-the-art methods. The experimental result presented here shows that the proposed method performs better.


Poisson distribution Global contrast Gaussian background suppression model Feature integration Salient object detection Regional saliency 



  1. 1.
    Durand, T., Mordan, T., Thome, N., Cord, M.: Wildcat: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (2017)Google Scholar
  2. 2.
    Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study. IEEE Trans. Image Process. 22(1), 55–69 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Mahasseni, B., Lam, M., Todorovic, S.: Unsupervised video summarization with adversarial lstm networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  4. 4.
    Lindeberg, T.: Image matching using generalized scale-space interest points. J. Math. Imaging Vis. 52(1), 3–36 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Demirci, M.F., Platel, B., Shokoufandeh, A., Florack, L.L., Dickinson, S.J.: The representation and matching of images using top points. J. Math. Imaging Vis. 35(2), 103–116 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Itti, L., Rees, G., Tsotsos, J.K.: Neurobiology of Attention. Elsevier, Amsterdam (2005)Google Scholar
  7. 7.
    Deng, X., Zuo, F., Li, H.: Cracks detection using iterative phase congruency. J. Math. Imaging Vis. 60(7), 1065–1080 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., Fulham, M., Feng, D.D.: Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J. Biomed. Health Inf. 21(6), 1685–1693 (2017)CrossRefGoogle Scholar
  9. 9.
    Wolfe, J.M., Cave, K.R., Franzel, S.L.: Guided search: an alternative to the feature integration model for visual search. J. Exp. Psychol. Hum. Percept. Perform. 15(3), 419 (1989)CrossRefGoogle Scholar
  10. 10.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  11. 11.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  12. 12.
    Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp 136–145 (2017)Google Scholar
  13. 13.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE 12th International Conference on Computer Vision, 2009, pp 2106–2113. IEEE (2009)Google Scholar
  14. 14.
    Kavak, Y., Erdem, E., Erdem, A.: A comparative study for feature integration strategies in dynamic saliency estimation. Signal Process. Image Commun. 51, 13–25 (2017)CrossRefGoogle Scholar
  15. 15.
    Yang, J., Yang, M.H.: Top-down visual saliency via joint CRF and dictionary learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2296–2303. IEEE (2012)Google Scholar
  16. 16.
    Qi, J., Dong, S., Huang, F., Lu, H.: Saliency detection via joint modeling global shape and local consistency. Neurocomputing 222, 81–90 (2017)CrossRefGoogle Scholar
  17. 17.
    Donoser, M., Urschler, M., Hirzer, M., Bischof, H.: Saliency driven total variation segmentation. In: 2009 IEEE 12th International Conference on Computer Vision, pp 817–824. IEEE (2009)Google Scholar
  18. 18.
    Zhu, C., Li, G., Wang, W., Wang, R.: An innovative salient object detection using center-dark channel prior. In: IEEE International Conference on Computer Vision Workshop (ICCVW) (2017)Google Scholar
  19. 19.
    Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRefGoogle Scholar
  20. 20.
    Zhang, J., Ehinger, K.A., Wei, H., Zhang, K., Yang, J.: A novel graph-based optimization framework for salient object detection. Pattern Recognit. 64, 39–50 (2017)CrossRefGoogle Scholar
  21. 21.
    Huang, X., Zhang, Y.J.: 300-FPS salient object detection via minimum directional contrast. IEEE Trans. Image Process. 26(9), 4243–4254 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Oh, K., Lee, M., Kim, G., Kim, S.: Detection of multiple salient objects through the integration of estimated foreground clues. Image Vis. Comput. 54, 31–44 (2016)CrossRefGoogle Scholar
  23. 23.
    Kienzle, W., Franz, M.O., Schölkopf, B., Wichmann, F.A.: Center-surround patterns emerge as optimal predictors for human saccade targets. J Vis. 9(5), 7–7 (2009)CrossRefGoogle Scholar
  24. 24.
    Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)Google Scholar
  25. 25.
    Huang, K., Zhu, C., Li, G.: Robust saliency detection via fusing foreground and background priors. arXiv preprint arXiv:1711.00322 (2017)
  26. 26.
    Tu, W.C., He, S., Yang, Q., Chien, S.Y.: Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2334–2342 (2016)Google Scholar
  27. 27.
    Zhang, J., Sclaroff, S.: Exploiting surroundedness for saliency detection: a boolean map approach. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 889–902 (2016)CrossRefGoogle Scholar
  28. 28.
    Cheng, Y., Fu, H., Wei, X., Xiao, J., Cao, X.: Depth enhanced saliency detection method. In: Proceedings of International Conference on Internet Multimedia Computing and Service, p. 23. ACM (2014)Google Scholar
  29. 29.
    Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)CrossRefGoogle Scholar
  30. 30.
    Chikkerur, S., Serre, T., Tan, C., Poggio, T.: What and where: a bayesian inference theory of attention. Vis. Res. 50(22), 2233–2247 (2010)CrossRefGoogle Scholar
  31. 31.
    Ren, J., Liu, Z., Zhou, X., Sun, G., Bai, C.: Saliency integration driven by similar images. J. Vis. Commun. Image Represent. 50, 227–236 (2018)CrossRefGoogle Scholar
  32. 32.
    Gao, G., Han, C., Ma, K., Liu, C.H., Ding, G., Liu, E.: Optimal feature combination analysis for crowd saliency prediction. J. Vis. Commun. Image Represent. 50, 1–8 (2018)CrossRefGoogle Scholar
  33. 33.
    Zeqiri, B.: Priming of visual attention in dynamic visual scenes-an experimental study using eye tracking. In: MEi: CogSci Conference 2013, Budapest (2013)Google Scholar
  34. 34.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597–1604. IEEE (2009)Google Scholar
  35. 35.
    Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 374–381. ACM (2003)Google Scholar
  36. 36.
    Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vis. Res. 49(10), 1295–1306 (2009)CrossRefGoogle Scholar
  37. 37.
    Yu, Y., Choi, J., Kim, Y., Yoo, K., Lee, S.H., Kim, G.: Supervising neural attention models for video captioning by human gaze data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, pp. 2680–2688 (2017)Google Scholar
  38. 38.
    Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict human fixations, MIT Technical Report (2012)Google Scholar
  39. 39.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  40. 40.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. In: ACM Transactions on Graphics (TOG), vol 23, pp. 309–314. ACM (2004)Google Scholar
  41. 41.
    Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Salient object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1, 1–1 (2014)Google Scholar
  42. 42.
    Tepper, M., Musé, P., Almansa, A.: On the role of contrast and regularity in perceptual boundary saliency. J. Math. Imaging Vis. 48(3), 396–412 (2014)CrossRefzbMATHGoogle Scholar
  43. 43.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Ahn, E., Lee, S., Kim, G.J.: Real-time adjustment of contrast saliency for improved information visibility in mobile augmented reality. Virtual Real. 22(3), 245–262 (2018)CrossRefGoogle Scholar
  45. 45.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR’07 IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8. IEEE (2007)Google Scholar
  46. 46.
    Lv, Q., Wang, B., Zhang, L.: Saliency computation via whitened frequency band selection. Cogn. Neurodyn. 10(3), 255–267 (2016)CrossRefGoogle Scholar
  47. 47.
    Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: International Conference on Computer Vision Systems, pp. 66–75. Springer (2008)Google Scholar
  48. 48.
    Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 478–485. IEEE (2012)Google Scholar
  49. 49.
    Huang, X., Zhang, Y.: Water flow driven salient object detection at 180 fps. Pattern Recognit. 76, 95–107 (2018)CrossRefGoogle Scholar
  50. 50.
    Borji, A., Cheng, M.M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey. arXiv preprint arXiv:1411.5878 (2014)
  51. 51.
    Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2653–2656. IEEE (2010)Google Scholar
  53. 53.
    Liu, F., Gleicher, M.: Region enhanced scale-invariant saliency detection. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 1477–1480. IEEE (2006)Google Scholar
  54. 54.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733–740. IEEE (2012)Google Scholar
  55. 55.
    Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1529–1536. IEEE (2013)Google Scholar
  56. 56.
    Zhang, L., Yang, C., Lu, H., Ruan, X., Yang, M.H.: Ranking saliency. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1892–1904 (2017)CrossRefGoogle Scholar
  57. 57.
    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3166–3173. IEEE (2013)Google Scholar
  58. 58.
    Zhang, L., Ai, J., Jiang, B., Lu, H., Li, X.: Saliency detection via absorbing Markov chain with learnt transition probability. IEEE Trans. Image Process. 27(2), 987–998 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2016)CrossRefGoogle Scholar
  60. 60.
    Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 110–119. IEEE (2015)Google Scholar
  61. 61.
    Cheng, G., Han, J., Zhou, P., Xu, D.: Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE Trans. Image Process. 28(1), 265–278 (2019)MathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)Google Scholar
  63. 63.
    Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)Google Scholar
  64. 64.
    Dong, S., Gao, Z., Sun, S., Wang, X., Li, M., Zhang, H., Yang, G., Liu, H., Li, S.: Holistic and deep feature pyramids for saliency detection. In: British Machine Vision Conference (BMVC), pp. 3–6. Northumbria University, Newcastle (2018)Google Scholar
  65. 65.
    Wang, T., Borji, A., Zhang, L., Zhang, P., Lu, H.: A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4019–4028 (2017)Google Scholar
  66. 66.
    Han, J., Chen, H., Liu, N., Yan, C., Li, X.: CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Trans. Cybern. 99, 1–13 (2017)Google Scholar
  67. 67.
    Li, M., Dong, S., Zhang, K., Gao, Z., Wu, X., Zhang, H., Yang, G., Li, S.: Deep learning intra-image and inter-images features for co-saliency detection (2018)Google Scholar
  68. 68.
    Han, J., Cheng, G., Li, Z., Zhang, D.: A unified metric learning-based framework for co-saliency detection. IEEE Trans. Circuits Syst. Video Technol. 28(10), 2473–2483 (2018)CrossRefGoogle Scholar
  69. 69.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. (TOG) 22(3), 313–318 (2003)CrossRefGoogle Scholar
  70. 70.
    Harremoës, P.: Binomial and Poisson distributions as maximum entropy distributions. IEEE Trans. Inf. Theory 47(5), 2039–2041 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  71. 71.
    Kourtzi, Z., Kanwisher, N.: Representation of perceived object shape by the human lateral occipital complex. Science 293(5534), 1506–1509 (2001)CrossRefGoogle Scholar
  72. 72.
    Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: a bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32–32 (2008)CrossRefGoogle Scholar
  73. 73.
    Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. Georgia Institute of Technology, Atlanta (2014)Google Scholar
  74. 74.
    Li, X., Li, Y., Shen, C., Dick, A., Van Den Hengel, A.: Contextual hypergraph modeling for salient object detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3328–3335. IEEE (2013a)Google Scholar
  75. 75.
    Li, J., Levine, M.D., An, X., Xu, X., He, H.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013b)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (Banaras Hindu University)VaranasiIndia

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