Reading Users’ Minds from Their Eyes: A Method for Implicit Image Annotation
Arti R.Bhore1, N.D.Kale2
1Arti R. Bhore, Computer Department, Pune University, TCOER College, K.J.s Institutes, Pune, India.
2Prof. N.D. Kale, Computer Department, Pune University, PVPIT College, JSPM Institutes, Pune, India.
Manuscript received on May 12, 2013. | Revised Manuscript received on June 13, 2013. | Manuscript published on June 30, 2013. | PP: 36-38 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1652062513/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper explores the possible solutions for image annotation and retrieval by implicitly monitoring user attention via eye-tracking. Features are extracted from the gaze trajectory of users examining sets of images to provide implicit information on the target template that guides visual attention. Here Gaze Inference System (GIS) is a fuzzy logic based framework that analyzes the gaze-movement features to assign a user interest level (UIL) from 0 to 1 to every image that appeared on the screen. Because some properties of the gaze features are unique for every user, our user adaptive framework builds a new processing system for every new user to achieve higher accuracy. The generated UILs can be used for image annotation purposes; however, the output of this system is not limited as it can be used also for retrieval or other scenarios. The developed framework produces promising and reliable UILs where approximately 53% of target images in the users’ minds can be identified by the machine with an error of less than 20% and the top 10% of them with no error. As show in this paper that the existing information in gaze patterns can be employed to improve the machine’s judgment of image content by assessment of human interest and attention to the objects inside virtual environments.
Keywords: Eye tracking framework, image annotation, image retrieval, Gaze Interface System.