An eye tracking interface for image search

ABSTRACT

Eye tracking presents an adaptive approach that can capture the user's current needs and tailor the retrieval accordingly. Applying eye tracking to image retrieval requires that new strategies be devised that can use visual and algorithmic data to obtain natural and rapid retrieval of images. Recent work showed that the eye is faster than the mouse as a source of visual input in a target image identification task [Oyekoya and Stentiford 2005]. We explore the viability of using the eye to drive an image retrieval interface. In a visual search task, users are asked to find a target image in a database and the number of steps to the target image are counted. It is reasonable to believe that users will look at the objects in which they are interested during a search [Oyekoya and Stentiford 2004] and this provides the machine with the necessary information to retrieve a succession of plausible candidate images for the user.

REFERENCES

• 1: Oyewole Oyekoya , Fred Stentiford, Exploring Human Eye Behaviour using a Model of Visual Attention, Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4, p.945-948, August 23-26, 2004 [doi>10.1109/ICPR.2004.353]
• 2: Oyekoya O. K., Stentiford F. W. M. 2005. A Performance Comparison of Eye Tracking and Mouse Interfaces in a Target Image Identification Task. European Workshop on the Integration of Knowledge, Semantics & Digital Media Technology, London, 30th Nov - 1st Dec, 2005.
• 3: Stentiford F. W. M. 2003. An attention based similarity measure with application to content based information retrieval, in Storage and Retrieval for Media Databases 2003, M. M. Yeung, R. W. Lienhart, C-S Li, Editors, Proc SPIE Vol. 5021, 20-24 Jan, Santa Clara.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Tracking

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.7 Feature Measurement
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Feature evaluation and selection; Pattern analysis
      I.5.4 Applications
          Subjects: Computer vision

General Terms:
Algorithms, Design, Measurement, Theory