Benefiting from users' gaze: selection of image regions from eye tracking information for provided tags

Providing image annotations is a tedious task. This becomes even more cumbersome when objects shall be annotated in the images. Such region-based annotations can be used in various ways like similarity search or as training set in automatic object detection. We investigate the principle idea of finding objects in images by looking at gaze paths from users, viewing images with an interest in a specific object. We have analyzed 799 gaze paths from 30 subjects viewing image-tag-pairs with the task to decide whether a tag could be found in the image or not. We have compared 13 different fixation measures analyzing the gaze paths. The best performing fixation measure is able to correctly assign a tag to a region for 63 % of the image-tag-pairs and significantly outperforms three baselines. We look into details of the image region characteristics such as the position and size for incorrect and correct assignments. The influence of aggregating multiple gaze paths from several subjects with respect to improving the precision of identifying the correct regions is also investigated. In addition, we look into the possibilities of discriminating different regions in the same image. Here, we are able to correctly identify two regions in the same image from different primings with an accuracy of 38 %.