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Guiding a Bottom-Up Visual Attention Mechanism to Locate Specific Image Regions Using a Distributed Genetic Optimization

  • Eanes T. Pereira
  • Herman M. Gomes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

The purpose of this paper is to present an approach to locate specific regions in images. The novelty of the approach is the combination of a weighted bottom-up visual attention mechanism with a genetic algorithm optimization running on a computational grid. The visual attention mechanism is based on the model proposed by Itti and Koch [1]. A saliency map indicates the most interesting points in an image using a number of intermediate low level features, which are detected at different scales and orientations. Using the saliency map weights as parameters, the optimization problem is to minimize the number of most salient points needed to locate a set of reference image regions, previously (and manually) labeled as being interesting. Both an objective and subjective evaluation have demonstrated that the proposed approach is more effective when compared to a fixed weight attention mechanism.

Keywords

Genetic Algorithm Visual Attention Detection Module Salient Point Salient Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Itti, L., Koch., C.: Computational Modeling of Visual Attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  2. 2.
    Tsotsos, J.: Analyzing Vision at the Complexity Level. The Behavioral and Brain Sciences 13(3), 423–445 (1990)Google Scholar
  3. 3.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)Google Scholar
  4. 4.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10-12), 1489–1506 (2000)CrossRefGoogle Scholar
  5. 5.
    Itti, L., Koch, C.: A comparison of feature combination strategies for saliency-based visual attention systems. In: Proc. SPIE human vision and electronic imaging IV, San Jose, USA, pp. 473–482 (1999)Google Scholar
  6. 6.
    Itti, L.: Models of Bottom-Up Attention and Saliency. In: Itti, L., Rees, G., Tsotsos, J.K. (eds.) Neurobiology of Attention, pp. 576–582. Elsevier, San Diego (2005)CrossRefGoogle Scholar
  7. 7.
    Mardle, S., Pascoe, S.: An overview of genetic algorithms for the solution of optimisation problems. Computers in High Education Economics Review 3(1) (1999)Google Scholar
  8. 8.
    Stentiford, F.: An evolutionary programming approach to the simulation of visual attention. In: Proc. Congress on Evolutionary Computation, Seoul, Korea, pp. 851–858 (2001)Google Scholar
  9. 9.
    Treptow, A., Zell, A.: Combining Adaboost learning and evolutionary search to select features for real-time object detection. In: Proc. IEEE Congress on Evolutionary Computation, Portland, USA, pp. 2107–2113 (2004)Google Scholar
  10. 10.
    Pereira, E., Gomes, H., Florentino, V.: Bottom-up visual attention guided by genetic algorithm optimization. In: IASTED International Conference on Signal and Image Processing, Honolulu, USA (August 2006) (accepted)Google Scholar
  11. 11.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)CrossRefGoogle Scholar
  12. 12.
    Siagian, C., Ititi, L.: Biologically-Inspired Face Detection: Non-Brute-Force-Search Approach. In: First IEEE-CVPR International Workshop on Face Processing in Video, June 2004, pp. 62–69 (2004)Google Scholar
  13. 13.
    Itti, L., Koch, C.: Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging 10(1), 1–169 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eanes T. Pereira
    • 1
  • Herman M. Gomes
    • 1
  1. 1.Departamento de Sistemas e ComputaçãoUniversidade Federal de Campina GrandeCampina GrandeBrazil

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