June 2007
Volume 7, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   June 2007
Evaluating the ability of visual search models suggested for computer-vision to predict human performance
Author Affiliations
  • Yaffa Yeshurun
    Psychology Department, University of Haifa, Israel
  • Tamar Avraham
    Computer Science Department, Technion, Israel
  • Michael Lindenbaum
    Computer Science Department, Technion, Israel
Journal of Vision June 2007, Vol.7, 722. doi:10.1167/7.9.722
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      Yaffa Yeshurun, Tamar Avraham, Michael Lindenbaum; Evaluating the ability of visual search models suggested for computer-vision to predict human performance. Journal of Vision 2007;7(9):722. doi: 10.1167/7.9.722.

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Abstract

The COVER and FLNN algorithms were previously suggested for computer-vision visual search (Avraham and Lindenbaum, 2006). These computer-vision models capture the dependency of search difficulty on distracters' homogeneity and target-distracters similarity, as was suggested originally in Duncan and Humphreys (1989). In this study, we extended those models to account for internal-noise, and evaluated their ability to predict human search performance. In four experiments, observers searched for a tilted target presented among distracters of different orientations (orientation-search) or a gray target appearing among distracters of different colors (color-search). Distracters' homogeneity and target-distracters similarity were systematically manipulated. Search performance was then used to test our models. We compared our models to several prominent models of visual search including a SDT-based model (e.g., Palmer, Ames, and Lindsey 1993), the Temporal-Serial model (e.g., Bergen and Julesz 1983, Eckstein 1998), the saliency model (Rosenholtz 1999) and the Best-Normal model (Rosenholtz 2001). In comparison to these models of visual search, our models' predictions were the closest to human performance.

Yeshurun, Y. Avraham, T. Lindenbaum, M. (2007). Evaluating the ability of visual search models suggested for computer-vision to predict human performance [Abstract]. Journal of Vision, 7(9):722, 722a, http://journalofvision.org/7/9/722/, doi:10.1167/7.9.722. [CrossRef]
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