Role of Gestalt Principles in Selecting Attention Areas for Object Recognition

  • Jixiang Shen
  • Amitash Ojha
  • Minho Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


Human attention plays an important role in human visual system. We assume that the Gestalt law is one of important factors to guide human selective attention. In this paper, we present a series of studies in which we hypothesized that regions of image that get more attention in an object recognition task, confirm to one or more gestalt principles and subconsciously attract human attention which eventually help in object recognition. In our study, we collected attention parts of images by analyzing eye movement of participants. Then we compared Gestalt scores of high attention parts with those of nonattended random parts. Our results suggest that continuity and symmetry of features attract human attention. We argue that an approach to analyze parts with high Gestalt scores can yield better than analyzing random parts of image in object recognition.


Gestalt principle selective attention perception symmetry continuity regularity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, J.R.: Cognitive psychology and its implications, 6th edn., p. 519 (2004)Google Scholar
  2. 2.
    Erich Rome, F.S., Christensen, H.I.: Computational visual attention systems and their cognitive foundations: A survey. ACM Transactions on Applied Perception (TAP) 7(1), 6 (2010)Google Scholar
  3. 3.
    Eriksen, C., Hoffman, J.: Temporal and spatial characteristics of selective encoding from visual displays. Perception & Psychophysics, 201–204 (1972)Google Scholar
  4. 4.
    Eriksen, C., St James, J.: Visual attention within and around the field of focal attention: A zoom lens model. Perception & Psychophysics, 225–240 (1986)Google Scholar
  5. 5.
    James, W.: The principles of psychology. Harvard UP, Cambridge (1890)CrossRefGoogle Scholar
  6. 6.
    Park, S.-J., An, K.-H., Lee, M.: Saliency map model with adaptive masking based on independent component analysis. Neurocomputing 49, 417–422 (2002)CrossRefGoogle Scholar
  7. 7.
    Jeong, S., Ban, S.-W., Lee, M.: Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment. Neural Networks 21(10), 1420–1430 (2008)Google Scholar
  8. 8.
    Christianvon, E.: Übergestaltqualitäten. Vierteljahresschriftfür wissenschaftliche Philosophie, 249–292 (1890)Google Scholar
  9. 9.
    Vassilis, C.: Perception-Action Cycle: Models, Architectures, and Hardware, 1 (2011)Google Scholar
  10. 10.
    Todorovic, D.: Gestalt principles. Scholarpedia, 5345 (2008)Google Scholar
  11. 11.
    Beymer, D., Orton, P.Z., Russell, D.M.: An eye tracking study of how pictures influence online reading. In: Baranauskas, C., Abascal, J., Barbosa, S.D.J. (eds.) INTERACT 2007. LNCS, vol. 4663, pp. 456–460. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Dou Y, Kong L.: A novel approach based on saliency edges to contour detection. In: International Conference on Audio, Language and Image Processing, pp. 552–556. IEEE Press (2008) Google Scholar
  13. 13.
    Walther, D.: Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 41–63 (2005)Google Scholar
  14. 14.
    Rutishauser, U.: Is bottom-up attention useful for object recognition? In: 22th Conference of Computer of Society, vol. 2. IEEE Press, Washington (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jixiang Shen
    • 1
  • Amitash Ojha
    • 1
  • Minho Lee
    • 1
  1. 1.School of Electronics EngineeringKyungpook National UniversityTaeguSouth Korea

Personalised recommendations