The University of Southampton
University of Southampton Institutional Repository

Can relevance of images be inferred from eye movements?

Can relevance of images be inferred from eye movements?
Can relevance of images be inferred from eye movements?
Searching for images from a large collection is a difficult task for automated algorithms. Many current techniques rely on items which have been manually 'tagged' with descriptors. This situation is not ideal, as it is difficult to formulate the initial query, and navigate the large number of hits returned. In order to present relevant images to the user, many systems rely on an explicit feedback mechanism. A machine learning algorithm can be used to present a new set of relevant images to the user -- thus increasing hit rates. In this work we use eye movements to assist a user when performing such a task, and ask this basic question: "Is it possible to replace or complement scarce explicit feedback with implicit feedback inferred from various sensors not specifically designed for the task?" We give initial results on a range of tasks and experiments which extend those presented in the Multimedia Information Retrieval conference (MIR'08). In reasonably controlled setups, fairly simple eye movements’ features in conjunction with machine learning techniques are capable of judging the relevance of an image based on eye movements alone, without using any explicit feedback -- therefore potentially assisting the user in a task.
50
Pasupa, Kitsuchart
952ededb-8c97-41b7-a65b-6aba31de2669
Klami, Arto
7d24333d-4417-42ba-a188-c22dea625f1d
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
de Campos, Teófilo
5a114727-e965-47f6-bb67-fae042e0b9fe
Kaski, Samuel
87dad6b5-fda3-494f-84f9-1de967bf1458
Pasupa, Kitsuchart
952ededb-8c97-41b7-a65b-6aba31de2669
Klami, Arto
7d24333d-4417-42ba-a188-c22dea625f1d
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
de Campos, Teófilo
5a114727-e965-47f6-bb67-fae042e0b9fe
Kaski, Samuel
87dad6b5-fda3-494f-84f9-1de967bf1458

Pasupa, Kitsuchart, Klami, Arto, Saunders, Craig, de Campos, Teófilo and Kaski, Samuel (2009) Can relevance of images be inferred from eye movements? 15th European Conference on Eye Movements (ECEM'2009), United Kingdom. 23 - 27 Aug 2009. p. 50 .

Record type: Conference or Workshop Item (Other)

Abstract

Searching for images from a large collection is a difficult task for automated algorithms. Many current techniques rely on items which have been manually 'tagged' with descriptors. This situation is not ideal, as it is difficult to formulate the initial query, and navigate the large number of hits returned. In order to present relevant images to the user, many systems rely on an explicit feedback mechanism. A machine learning algorithm can be used to present a new set of relevant images to the user -- thus increasing hit rates. In this work we use eye movements to assist a user when performing such a task, and ask this basic question: "Is it possible to replace or complement scarce explicit feedback with implicit feedback inferred from various sensors not specifically designed for the task?" We give initial results on a range of tasks and experiments which extend those presented in the Multimedia Information Retrieval conference (MIR'08). In reasonably controlled setups, fairly simple eye movements’ features in conjunction with machine learning techniques are capable of judging the relevance of an image based on eye movements alone, without using any explicit feedback -- therefore potentially assisting the user in a task.

Full text not available from this repository.

More information

Published date: 24 August 2009
Additional Information: Event Dates: 23-27 August 2009
Venue - Dates: 15th European Conference on Eye Movements (ECEM'2009), United Kingdom, 2009-08-23 - 2009-08-27
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 267795
URI: https://eprints.soton.ac.uk/id/eprint/267795
PURE UUID: 561f4629-9c0b-4bb5-ba69-a0fd443a71c5

Catalogue record

Date deposited: 24 Aug 2009 16:30
Last modified: 18 Jul 2017 07:00

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×