Human Attention Estimation for Natural Images: An Automatic Gaze Refinement Approach
This work addresses the need for automated human attention estimation in personal photo collections, offering an incremental improvement over existing methods.
The paper tackles the problem of estimating human attention regions in natural images by integrating implicit gaze estimation with computational saliency models, enabling on-the-fly estimation without user participation.
Photo collections and its applications today attempt to reflect user interactions in various forms. Moreover, photo collections aim to capture the users' intention with minimum effort through applications capturing user intentions. Human interest regions in an image carry powerful information about the user's behavior and can be used in many photo applications. Research on human visual attention has been conducted in the form of gaze tracking and computational saliency models in the computer vision community, and has shown considerable progress. This paper presents an integration between implicit gaze estimation and computational saliency model to effectively estimate human attention regions in images on the fly. Furthermore, our method estimates human attention via implicit calibration and incremental model updating without any active participation from the user. We also present extensive analysis and possible applications for personal photo collections.