PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning relevant eye movement feature spaces across users
Zakria Hussain, Kitsuchart Pasupa and John Shawe-Taylor
In: 6th Biennial Symposium on Eye Tracking Research & Applications (ETRA'2010), 22-24 Mar 2010, Austin, TX, USA.

Abstract

In this paper we predict the relevance of images based on a low-dimensional feature space found using several users’ eye movements. Each user is given an image-based search task, during which their eye movements are extracted using a Tobii eye-tracker. The users also provide us with explicit feedback regarding the relevance of images. We demonstrate that by using a greedy Nystrom algorithm on the eye movement features of different users, we can find a suitable low-dimensional feature space for learning. We validate the suitability of this feature space by projecting the eye movement features of a new user into this space, training an online learning algorithm using these features, and showing that the number of mistakes (regret over time) made in predicting relevant images is lower than when using the original eye movement features. We also plot Recall-Precision and ROC curves, and use a sign test to verify the statistical significance of our results.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:5634
Deposited By:Kitsuchart Pasupa
Deposited On:08 March 2010