Identifying objects in images from analyzing the users’ gaze movements for provided tags

Abstract. Millions of users share, tag, and search for images on social media platforms and social networking sites today. Annotating and searching for specific image regions, however, is still very hard. Assuming that eye tracking will be a common input device in the near future in notebooks equipped with cameras and mobile devices like iPads, it is possible to implicitly gain information about images and image regions from these users ’ gaze movements. In this paper, we investigate the principle idea of finding specific objects shown in images by looking at the users ’ gaze path information only. We have analyzed 547 gaze paths from 20 subjects viewing different image-tag-pairs with the task to decide if the tag presented is actually found in the image or not. By analyzing the gaze paths, we are able to correctly identify 67 % of the image regions and significantly outperform two baselines. In addition, we have investigated if different regions of the same image can be differentiated by the gaze information. Here, we are able to correctly identify two different regions in the same image with an accuracy of 38%.

Interactive video retrieval using implicit user feedback.

PhDIn the recent years, the rapid development of digital technologies and the low cost of recording media have led to a great increase in the availability of multimedia content worldwide. This availability places the demand for the development of advanced search engines. Traditionally, manual annotation of video was one of the usual practices to support retrieval. However, the vast amounts of multimedia content make such practices very expensive in terms of human effort. At the same time, the availability of low cost wearable sensors delivers a plethora of user-machine interaction data. Therefore, there is an important challenge of exploiting implicit user feedback (such as user navigation patterns and eye movements) during interactive multimedia retrieval sessions with a view to improving video search engines. In this thesis, we focus on automatically annotating video content by exploiting aggregated implicit feedback of past users expressed as click-through data and gaze movements. Towards this goal, we have conducted interactive video retrieval experiments, in order to collect click-through and eye movement data in not strictly controlled environments. First, we generate semantic relations between the multimedia items by proposing a graph representation of aggregated past interaction data and exploit them to generate recommendations, as well as to improve content-based search. Then, we investigate the role of user gaze movements in interactive video retrieval and propose a methodology for inferring user interest by employing support vector machines and gaze movement-based features. Finally, we propose an automatic video annotation framework, which combines query clustering into topics by constructing gaze movement-driven random forests and temporally enhanced dominant sets, as well as video shot classification for predicting the relevance of viewed items with respect to a topic. The results show that exploiting heterogeneous implicit feedback from past users is of added value for future users of interactive video retrieval systems