Selective Visual Attention: Computational Models and Applications

3.5 Attention Modelling Based on Information Maximizing

The BS model and its variations are based on feature integration theory, and the GBVS model with graph theory (which was discussed in the previous section) also works in a similar framework, since they all need low-level feature extraction and integration. Their feature maps obtained by using filters and contrast processes are based on explicit extraction of intensity, colour, orientation and so on, and then the final saliency map is a cross-feature integration. The BS model is very successful for simulating human attentional focus in numerous experiments including artificial and natural scenes.

However, this definition of saliency based on local feature contrast may be somewhat questionable since some discarded regions from the feature extracting process in the BS model are possibly fixation locations [7, 34]. For instance, in Figure 3.10(a) many long bars of various colours with random orientation are almost stacked over the full scene except for a small homogeneous region and in Figure 3.10(b), in a regular bar array an absent region appears. Very often, these unique regions in their respective scenes are not detected by these filters in the BS model since there are no orientation or colour features in these regions, but their uniqueness attracts the fixation of human eyes. In the view of information theory, these unique regions have more information than other regions with repeating or random objects.

Figure 3.10 Examples ...