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The GazeCom project is funded by the European Commission (contract no. IST-C-033816) within the Information Society Technologies (IST) priority of the 6th Framework Programme.
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A machine learning framework for gaze guidance

by Eleonora Vig last modified 2010-09-03 16:33

A machine learning framework for gaze guidance (Presented at the European Conference on Visual Perception 2009)

Eleonora Vig,  Michael Dorr, Karl Gegenfurtner, and Erhardt Barth

What constitutes the difference between fixated and non-fixated movie
patches? How can we change a patch to make it more or less salient? Here, we present a novel computational model of low-level saliency with dual emphasis: the same machine learning framework is used (i) for predicting saccade targets in natural dynamic scenes, and (ii) for learning how to alter the saliency level of these targets.

We use a large data set of eye movements on high-resolution videos of natural scenes. The 40,000 detected saccades are used to label movie patches as attended and non-attended. The proposed saliency measure, spectral energy, is computed in the neighborhood of each location on each scale of an anisotropic spatio-temporal Laplacian pyramid. On this simple low-dimensional representation of a patch (only one value, the spectral energy, per scale) we train a support vector machine, which outperforms state-of-the-art saliency predictors, reaching an ROC score of 0.8. Furthermore, we use this classifier to derive transformations in the energy profiles that alter the saliency distribution of the scene. Preliminary results show that gaze-contingent energy modifications do indeed have a gaze guiding effect.

Poster in pdf format.


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