Visual attention based ROI maps from gaze tracking data

The use of visual attention (VA) spatial and temporal characteristics, monitored by a gaze-tracking device, to generate a region of interest (ROI) ´importance´ map is proposed. A K-means clustering approach is adopted to group gaze location points into a number of clusters to represent the loci of regions of VA (or ROIs). Several metrics are then derived from the gaze positions and sequences to quantify the relative importance of the K-means clusters. An entropy-weighting strategy is adopted for the combination of these metrics to generate the ROI map. Results show that the ROI map is robust to the number of clusters and different gaze patterns, and can be used in progressive image coding/decoding to enhance the image quality in regions of interest.