Left: for each video frame, the Lasso is used to learn the visual feature weights that best fit the eye position density map. At the bottom right, the lasso output show different sets of weights for different values of the regularization parameter. The weights leading to the model with the smallest Bayesian Information Criterion (BIC) is chosen.
Right: temporal evolution of the best set of feature weights (static saliency, dynamic saliency, center bias,uniform map, face1 and face2).
Comparison of the time-dependent fusion using the Lasso weights (bottom left) with the time-independent fusion proposed in Marat 2013 (bottom right). The features used are static saliency, dynamic saliency, faces, center bias, and uniform map.