Predicting Eye Fixations on Complex Visual Stimuli Using Local Symmetry

Most bottom-up models that predict human eye fixations are based on contrast features. The saliency model of Itti, Koch and Niebur is an example of such contrast-saliency models. Although the model has been successfully compared to human eye fixations, we show that it lacks preciseness in the prediction of fixations on mirror-symmetrical forms. The contrast model gives high response at the borders, whereas human observers consistently look at the symmetrical center of these forms. We propose a saliency model that predicts eye fixations using local mirror symmetry. To test the model, we performed an eye-tracking experiment with participants viewing complex photographic images and compared the data with our symmetry model and the contrast model. The results show that our symmetry model predicts human eye fixations significantly better on a wide variety of images including many that are not selected for their symmetrical content. Moreover, our results show that especially early fixations are on highly symmetrical areas of the images. We conclude that symmetry is a strong predictor of human eye fixations and that it can be used as a predictor of the order of fixation

The relation of phase noise and luminance contrast to overt attention in complex visual stimuli

Models of attention are typically based on difference maps in low-level features but neglect higher order stimulus structure. To what extent does higher order statistics affect human attention in natural stimuli? We recorded eye movements while observers viewed unmodified and modified images of natural scenes. Modifications included contrast modulations (resulting in changes to first- and second-order statistics), as well as the addition of noise to the Fourier phase (resulting in changes to higher order statistics). We have the following findings: (1) Subjects' interpretation of a stimulus as a “natural” depiction of an outdoor scene depends on higher order statistics in a highly nonlinear, categorical fashion. (2) Confirming previous findings, contrast is elevated at fixated locations for a variety of stimulus categories. In addition, we find that the size of this elevation depends on higher order statistics and reduces with increasing phase noise. (3) Global modulations of contrast bias eye position toward high contrasts, consistent with a linear effect of contrast on fixation probability. This bias is independent of phase noise. (4) Small patches of locally decreased contrast repel eye position less than large patches of the same aggregate area, irrespective of phase noise. Our findings provide evidence that deviations from surrounding statistics, rather than contrast per se, underlie the well-established relation of contrast to fixation