Advertisement

Top-Down Biasing and Modulation for Object-Based Visual Attention

  • Alcides Xavier Benicasa
  • Marcos G. Quiles
  • Liang Zhao
  • Roseli A. F. Romero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

This work presents a new object-based visual attention model with bottom-up and top-down features. Bottom-up attention is related to the contrast of primitive visual features, such as color, orientation, and intensity. On the other hand, top-down attention is related to the intentions of the viewer and can be seen as a modulation process through the selection system. Thus, if the viewer is searching for an specific shape or color, the top-down modulation can bias the searching process in relation to those features. Our model is composed of five main modules which are responsible for the extraction of the visual features, image segmentation, object recognition, object-saliency map, and object selection. Results on natural images are compared with state-of-the-art approaches and an ground truth fixation maps for a variety of images revealing the efficacy of the proposed approach for visual attention.

Keywords

top-down biasing bottom-up and top-down visual attention object-based attention recognition of objects 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned Salient Region Detection. In: IEEE CVPR, pp. 1597–1604 (2009)Google Scholar
  2. 2.
    Benicasa, A.X., Zhao, L., Romero, R.A.F.: Model of top-down / bottom-up visual attention for location of salient objects in specific domains. In: IEEE IJCNN (2012)Google Scholar
  3. 3.
    Benicasa, A.X., Quiles, M.G., Zhao, L., Romero, R.A.F.: An object-based visual selection model with bottom-up and top-down modulations. In: SBRN (2012)Google Scholar
  4. 4.
    Benicasa, A.X., Romero, R.A.F.: Localization of salient objects in scenes through visual attention. In: SBRN (2010)Google Scholar
  5. 5.
    Borji, A., Ahmadabadi, N.M., Araabi, B.N.: Cost-sensitive learning of top-down modulation for attentional control. MVA 22(1), 61–76 (2011)Google Scholar
  6. 6.
    Borji, A., Sihite, D.N., Itti, L.: Salient object detection: A benchmark. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 414–429. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9(3), 1–24 (2009)CrossRefGoogle Scholar
  8. 8.
    Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. In: IEEE CVPR, pp. 409–416 (2011)Google Scholar
  9. 9.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annual Review of Neuroscience 18, 193–222 (1995)CrossRefGoogle Scholar
  10. 10.
    Elazary, L., Itti, L.: A bayesian model for efficient visual search and recognition. Vision Research 50(14), 1338–1352 (2010)CrossRefGoogle Scholar
  11. 11.
    Frintrop, S., Rome, E., Christensen, H.I.: Computational visual attention systems and their cognitive foundations: A survey. ACM TAP 7(1), 1–6 (2010)CrossRefGoogle Scholar
  12. 12.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2, 194–203 (2001)CrossRefGoogle Scholar
  13. 13.
    Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict human fixations. MIT Computer Science and AI (2012)Google Scholar
  14. 14.
    Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimal object detection. In: IEEE CVPR (2006)Google Scholar
  15. 15.
    Quiles, M.G., Wang, D., Zhao, L., Romero, R.A.F., Huang, D.-S.: Selecting salient objects in real scenes: An oscillatory correlation model. Neural Networks 24(1), 54–64 (2011)CrossRefGoogle Scholar
  16. 16.
    Silva, T.C., Zhao, L.: Network-based high level data classification. IEEE Transactions on Neural Networks 23, 954–970 (2012)CrossRefGoogle Scholar
  17. 17.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Wang, D., Terman, D.: Image segmentation based on oscillatory correlation. Neural Computation 9, 805–836 (1997)CrossRefGoogle Scholar
  19. 19.
    Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nature R. Neuroscience 5, 495–501 (2004)CrossRefGoogle Scholar
  20. 20.
    Yantis, S.: Goal-directed and stimulus-driven determinants of attentional control, vol. 18, pp. 73–103. MIT Press, Cambridge (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alcides Xavier Benicasa
    • 1
  • Marcos G. Quiles
    • 2
  • Liang Zhao
    • 3
  • Roseli A. F. Romero
    • 3
  1. 1.Federal University of SergipeItabaianaBrazil
  2. 2.Federal University of São PauloSão PauloBrazil
  3. 3.University of São PauloSão CarlosBrazil

Personalised recommendations