Advertisement

Boolean Map Saliency: A Surprisingly Simple Method

  • Jianming Zhang
  • Filip Malmberg
  • Stan Sclaroff
Chapter

Abstract

In this chapter, we propose a simple yet powerful saliency detection model for eye fixation prediction based on the surroundedness cue for figure-ground segregation. The essence of surroundedness is the enclosure topological relationship between different visual components. This kind of topological relationship is invariant under homeomorphisms; thus it is a quite fundamental property of a scene, regardless of the scale or the shape of the visual content. It is also worth noting that the topological status of a scene has long been identified as one of the probable attributes that guide the deployment of visual attention.

References

  1. 8.
    Ba, J., Mnih, V., and Kavukcuoglu, K. Multiple object recognition with visual attention. In International Conference on Learning Representations (ICLR) (2015).Google Scholar
  2. 9.
    Barinova, O., Lempitsky, V., and Kholi, P. On detection of multiple object instances using Hough transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 9 (2012), 1773–1784.CrossRefGoogle Scholar
  3. 13.
    Borji, A., and Itti, L. Exploiting local and global patch rarities for saliency detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012).Google Scholar
  4. 14.
    Borji, A., and Itti, L. State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 35, 1 (2013), 185–207.CrossRefGoogle Scholar
  5. 16.
    Borji, A., Tavakoli, H. R., Sihite, D. N., and Itti, L. Analysis of scores, datasets, and models in visual saliency prediction. In IEEE International Conference on Computer Vision (ICCV) (2013).Google Scholar
  6. 19.
    Bruce, N. D., and Tsotsos, J. K. Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9, 3 (2009), 5.CrossRefGoogle Scholar
  7. 22.
    Cerf, M., Harel, J., Einhäuser, W., and Koch, C. Predicting human gaze using low-level saliency combined with face detection. In Advances in Neural Information Processing Systems (NIPS) (2008).Google Scholar
  8. 27.
    Chen, C., Tang, H., Lyu, Z., Liang, H., Shang, J., and Serem, M. Saliency modeling via outlier detection. Journal of Electronic Imaging 23, 5 (2014), 053023–053023.CrossRefGoogle Scholar
  9. 28.
    Chen, J., Jin, Q., Yu, Y., and Hauptmann, A. G. Image profiling for history events on the fly. In ACM International Conference on Multimedia (2015).Google Scholar
  10. 29.
    Chen, L. Topological structure in visual perception. Science 218 (1982), 699.CrossRefGoogle Scholar
  11. 30.
    Chen, T., Cheng, M.-M., Tan, P., Shamir, A., and Hu, S.-M. Sketch2photo: internet image montage. ACM Transactions on Graphics (TOG) 28, 5 (2009), 124.Google Scholar
  12. 52.
    Devroye, L. Non-uniform random variate generation. New York: Springer-Verlag, 1986.CrossRefGoogle Scholar
  13. 53.
    Duan, L., Wu, C., Miao, J., Qing, L., and Fu, Y. Visual saliency detection by spatially weighted dissimilarity. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011).Google Scholar
  14. 54.
    Erdem, E., and Erdem, A. Visual saliency estimation by nonlinearly integrating features using region covariances. Journal of vision 13, 4 (2013), 11.CrossRefGoogle Scholar
  15. 61.
    Garcia-Diaz, A., Fdez-Vidal, X. R., Pardo, X. M., and Dosil, R. Saliency from hierarchical adaptation through decorrelation and variance normalization. Image and Vision Computing 30, 1 (2012), 51–64.CrossRefGoogle Scholar
  16. 64.
    Goferman, S., Zelnik-Manor, L., and Tal, A. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 10 (2012), 1915–1926.CrossRefGoogle Scholar
  17. 70.
    Han, X., Satoh, S., Nakamura, D., and Urabe, K. Unifying computational models for visual attention. In INCF Japan Node International Workshop: Advances in Neuroinformatics (2014).Google Scholar
  18. 71.
    Harel, J., Koch, C., and Perona, P. Graph-based visual saliency. In Advances in Neural Information Processing Systems (NIPS) (2007).Google Scholar
  19. 74.
    Hou, X., Harel, J., and Koch, C. Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34, 1 (2012), 194–201.Google Scholar
  20. 75.
    Hou, X., and Zhang, L. Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007).Google Scholar
  21. 77.
    Huang, L., and Pashler, H. A Boolean map theory of visual attention. Psychological Review 114, 3 (2007), 599.CrossRefGoogle Scholar
  22. 78.
    Itti, L., and Baldi, P. Bayesian surprise attracts human attention. In Advances in Neural Information Processing Systems (NIPS) (2006).Google Scholar
  23. 79.
    Itti, L., Koch, C., and Niebur, E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 20, 11 (1998), 1254–1259.CrossRefGoogle Scholar
  24. 86.
    Judd, T., Durand, F., and Torralba, A. A benchmark of computational models of saliency to predict human fixations. In MIT Technical Report (2012).Google Scholar
  25. 87.
    Judd, T., Ehinger, K., Durand, F., and Torralba, A. Learning to predict where humans look. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009).Google Scholar
  26. 93.
    Kienzle, W., Wichmann, F., Schölkopf, B., and Franz, M. A nonparametric approach to bottom-up visual saliency. In Advances in Neural Information Processing Systems (NIPS) (2007).Google Scholar
  27. 97.
    Kootstra, G., Nederveen, A., and De Boer, B. Paying attention to symmetry. In BMVC (2008).Google Scholar
  28. 101.
    Kümmerer, M., Theis, L., and Bethge, M. Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet. In International Conference on Learning Representations (ICLR) Workshop (2015).Google Scholar
  29. 103.
    Le Moan, S., and Farup, I. Exploiting change blindness for image compression. In IEEE International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (2015).Google Scholar
  30. 107.
    Li, J., Levine, M. D., An, X., Xu, X., and He, H. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 35, 4 (2013), 996–1010.CrossRefGoogle Scholar
  31. 114.
    Lu, S., Tan, C., and Lim, J. Robust and efficient saliency modeling from image co-occurrence histograms. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 36, 1 (2014), 195–201.Google Scholar
  32. 117.
    Ma, C., Miao, Z., and Li, M. Saliency weighted spatial pyramid representation for object recognition. In IET International Conference on Wireless, Mobile and Multi-Media) (2015), IET.Google Scholar
  33. 133.
    Palmer, S. E. Vision Science: Photons to Phenomenology. The MIT press, 1999.Google Scholar
  34. 135.
    Peters, R. J., Iyer, A., Itti, L., and Koch, C. Components of bottom-up gaze allocation in natural images. Vision Research 45, 18 (2005), 2397–2416.CrossRefGoogle Scholar
  35. 142.
    Rosenholtz, R. Search asymmetries? what search asymmetries? Perception & Psychophysics 63, 3 (2001), 476–489.CrossRefGoogle Scholar
  36. 153.
    Schauerte, B., and Stiefelhagen, R. Quaternion-based spectral saliency detection for eye fixation prediction. In European Conference on Computer Vision (ECCV) (2012).CrossRefGoogle Scholar
  37. 154.
    Schenk, F., Urschler, M., Aigner, C., Roesner, I., Aichinger, P., and Bischof, H. Automatic glottis segmentation from laryngeal high-speed videos using 3d active contours. In Medical Image Understanding and Analysis Conference (2014), pp. 111–116.Google Scholar
  38. 156.
    Seo, H. J., and Milanfar, P. Static and space-time visual saliency detection by self-resemblance. Journal of vision 9, 12 (2009), 15.CrossRefGoogle Scholar
  39. 163.
    Sobral, A., Bouwmans, T., and ZahZah, E.-h. Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on (2015), IEEE, pp. 1–6.Google Scholar
  40. 168.
    Sugano, Y., and Bulling, A. Self-calibrating head-mounted eye trackers using egocentric visual saliency. In Annual ACM Symposium on User Interface Software & Technology (2015).Google Scholar
  41. 174.
    Tatler, B., Baddeley, R., Gilchrist, I., et al. Visual correlates of fixation selection: Effects of scale and time. Vision Research 45, 5 (2005), 643–659.CrossRefGoogle Scholar
  42. 175.
    Tavakoli, H. R., Rahtu, E., and Heikkilä, J. Fast and efficient saliency detection using sparse sampling and kernel density estimation. In Image Analysis. Springer, 2011, pp. 666–675.Google Scholar
  43. 183.
    Vig, E., Dorr, M., and Cox, D. Large-scale optimization of hierarchical features for saliency prediction in natural images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).Google Scholar
  44. 189.
    Wolfe, J. M., and Horowitz, T. S. What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5, 6 (2004), 495–501.CrossRefGoogle Scholar
  45. 194.
    Yang, C., Zhang, L., Lu, H., Ruan, X., and Yang, M.-H. Saliency detection via graph-based manifold ranking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).Google Scholar
  46. 196.
    Yun, K., Peng, Y., Samaras, D., Zelinsky, G. J., and Berg, T. L. Studying relationships between human gaze, description, and computer vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013).Google Scholar
  47. 197.
    Zeng, W., Yang, M., and Cui, Z. Ultra-low bit rate facial coding hybrid model based on saliency detection. Journal of Image and Graphics 3, 1 (2015).CrossRefGoogle Scholar
  48. 201.
    Zhang, J., and Sclaroff, S. saliency detection: a Boolean map approach. In IEEE International Conference on Computer Vision (ICCV) (2013).Google Scholar
  49. 205.
    Zhang, L., Tong, M., Marks, T., Shan, H., and Cottrell, G. SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision 8, 7 (2008), 32.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jianming Zhang
    • 1
  • Filip Malmberg
    • 2
  • Stan Sclaroff
    • 3
  1. 1.Adobe Inc.San JoseUSA
  2. 2.Centre for Image AnalysisUppsala UniversityUppsalaSweden
  3. 3.Department of Computer ScienceBoston UniversityBostonUSA

Personalised recommendations