Feature selection in supervised saliency prediction

There is an increasing interest in learning mappings from features to saliency maps based on human fixation data on natural images. These models have achieved better results than most bottom-up (unsupervised) saliency models. However, they usually use a large set of features trying to account for all possible saliency-related factors, which increases time cost and leaves the truly effective features unknown. Through supervised feature selection, we show that the features used in existing models are highly redundant. On each of three benchmark datasets considered in this paper, a small number of features are found to be good enough for predicting human eye fixations in free viewing experiments. The resulting model achieves comparable results to that with all features and outperforms the state-of-the-art models on these datasets. In addition, both the features selected and the model trained on any dataset exhibit good performance on the other two datasets, indicating robustness of the selected features and models across different datasets. Finally, after training on a dataset for two different tasks, eye fixation prediction and salient object detection, the selected features show robustness across the two tasks. Taken together, these findings suggest that a small set of features could account for visual saliency.