GLASE 0.1
Viktors Garkavijs, Mayumi Toshima, Noriko Kando · 2012
This paper proposes a prototype system called Gaze-Learning-Access-and-Search-Engine 0.1 (GLASE), which can perform image relevance ranking based on gaze data and within-session learning. We developed a search user interface that uses an eye-tracker as an input device and employed a relevance re-ranking algorithm based on the gaze length. The preliminary experimental results showed that using our gaze-driven system reduced the task completion time an average of 13.7% in a search session.