Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics.

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.