The modeling of the human visual attention into a computational attention model leads to the split of visual features into several independent channels. Then, a difficult problem arises to combine these maps, having different dynamic ranges or distribution. When several maps are considered, such process is mandatory in order to compute a single measure of interest for each location, regardless of which features contributed to the salience. Several strategies of cue combination are proposed in this paper for the spatial cues as well as the temporal saliency. Finally, some user tests on still image and video databases leads to highlight one operator.

A purely bottom-up model of visual attention is proposed and compared to five state-of-the-art models. The role of the low-level visual features is examined in two contexts. Two datasets are used: one containing data coming from an eye tracking experiment obtained in a free-viewing task and a second containing 5000 hand-label pictures (observers had to enclose the most visually interesting objects in a rectangle). The relevancy of the bottom-up models, i.e. the ability of a model to predict where the salient areas are located, is evaluated. Whatever the metrics and the datasets, the degree of similarity between predictions and ground truth is significantly above chance. The proposed model, resting on a small number of features, is shown to be a good predictor of the human visual fixations but also a good predictor of the objects chosen as interesting by observers. This study suggests that the low-level of visual features have a significant role in a free-viewing task but also in a high-level visual task, such as the choice of the object of interest in a complex visual scene. Another outcome concerns the viewing duration used in eye tracking experiments. Results suggest that this parameter is finally not as critical as one would expect.