PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inferring task-relevant image regions from gaze data
Arto Klami
In: 2010 IEEE International Workshop on Machine Learning for Signal Processing, 29 Aug - 01 Sep 2010, Kittilä, Finland.


A number of studies have recently used eye movements of a user inspecting the content as implicit relevance feedback for proactive retrieval systems. Typically binary feedback for images or text paragraphs is inferred from the gaze pattern. We seek to make such feedback richer for image retrieval, by inferring which parts of the image the user found relevant. For this purpose, we present a novel Bayesian mixture model for inferring possible target regions directly from gaze data alone, and show how the relevance of those regions can then be inferred using a simple classifier that is independent of the content or the task.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:7607
Deposited By:Arto Klami
Deposited On:17 March 2011