EyeGrab: A Gaze-based Game with a Purpose to Enrich Image Context Information
Tina Walber, Chantal Neuhaus, Ansgar Scherp · 2012
We present EyeGrab, a game for image classification that is controlled by the users ' gaze. The players classify images according to their relevance for a given tag. Besides entertaining the players, the aim is to enrich the image context information to improve the image search in the future. During the game, information about the shown images is collected. It includes the classification concerning the tag, a rating of the given images by the user (“like ” or “not like”) and the eye tracking information recorded when viewing the images. In this work, we present the design of the game and compare two design variants – one with and one without visual aid – concerning the suitability of the game for image annotation. The variants of the game are evaluated in a study with 24 participants. We measured the user satisfaction, efficiency and effectiveness of the game. Overall, 83 % of the users enjoyed playing the game. The results show that the visual aid is not helping the users in our application; it even increases the error rate. The best classification precision we achieve is 92 % for the game variant without visual aid.