September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Tell me how you look and I will tell you what you are looking at
Author Affiliations
  • Antoine Coutrot
    CoMPLEX, University College London, UK
  • Nathalie Guyader
    Gipsa-lab, CNRS & Grenoble-Alpes University, France
Journal of Vision September 2015, Vol.15, 342. doi:10.1167/15.12.342
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      Antoine Coutrot, Nathalie Guyader; Tell me how you look and I will tell you what you are looking at. Journal of Vision 2015;15(12):342. doi: 10.1167/15.12.342.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The great ability of the human visual system to classify natural scenes has long been known. For instance, some objects (faces, animals) can be spotted in less time than the duration of a single fixation. Many studies also show that exploration strategies depend on many different high and low level features. However, the link between natural scene classification and eye movement parameters has rarely been explored. In this study, we built a base of 45 videos split into three visual categories: Landscapes (forest, meadow, seashore, etc.), Moving Objects (cars, plane, chain reactions, etc.), and Faces (one to four persons having a conversation). These categories were chosen because of the wide variety of regions of interest they contain. We tracked the eyes of 72 participants watching these videos using an Eyelink 1000 (SR Research). Firstly, univariate analyses show that visual categories substantially influence visual exploration, modify eye movement parameters (fixation duration, saccade amplitude, saccade direction), and impact on fixation locations (mean distance to centre, mean dispersion between the eye positions). Secondly, multivariate analyses were performed via Linear Discriminant Analysis (LDA) on the latter variables. The resulting vectors were used as a linear classifier. On the eye movements recorded with our stimuli, the accuracy of such a classifier reaches 86.7%. The contributions of this study are twofold. First, we quantified the deep influence that visual category of a natural scene has on eye movement parameters. As a consequence of these influences, some eye-tracking results obtained using stimuli belonging to a given visual category may not be generalised to other categories. Second, we showed that simple eye movement parameters are good predictors of the explored visual category. This has numerous applications in computer vision, including saliency-based compression algorithms adapted to the content of the scene.

Meeting abstract presented at VSS 2015

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