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A Comparison of Feature Detectors with Passive and Task-Based Visual Saliency

  • Patrick Harding
  • Neil M. Robertson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

This paper investigates the coincidence between six interest point detection methods (SIFT, MSER, Harris-Laplace, SURF, FAST & Kadir-Brady Saliency) with two robust “bottom-up” models of visual saliency (Itti and Harel) as well as “task” salient surfaces derived from observer eye-tracking data. Comprehensive statistics for all detectors vs. saliency models are presented in the presence and absence of a visual search task. It is found that SURF interest-points generate the highest coincidence with saliency and the overlap is superior by 15% for the SURF detector compared to other features. The overlap of image features with task saliency is found to be also distributed towards the salient regions. However the introduction of a specific search task creates high ambiguity in knowing how attention is shifted. It is found that the Kadir-Brady interest point is more resilient to this shift but is the least coincident overall.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrick Harding
    • 1
    • 2
  • Neil M. Robertson
    • 1
  1. 1.School of Engineering and Physical SciencesHeriot-Watt Univ.UK
  2. 2.Thales Optronics Ltd.UK

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