Graph-Based Joint Clustering of Fixations and Visual Entities
We present a method that extracts groups of fixations and image regions for the purpose of gaze analysis and image understanding. Since the attentional relationship between visual entities conveys rich information, automatically determining the relationship provides us a semantic representation of images. We show that, by jointly clustering human gaze and visual entities, it is possible to build meaningful and comprehensive metadata that offer an interpretation about how people see images. To achieve this, we developed a clustering method that uses a joint graph structure between fixation points and over-segmented image regions to ensure a cross-domain smoothness constraint. We show that the proposed clustering method achieves better performance in relating attention to visual entities in comparison with standard clustering techniques.
clustering gaze analysis human factors image segmentation roi detection gaze visualization