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Learning to Rank Images from Eye Movements

Record type: Conference or Workshop Item (Other)

Combining multiple information sources can improve the accuracy of search in information retrieval. This paper presents a new image search strategy which combines image features together with implicit feedback from users' eye movements, using them to rank images. In order to better deal with larger data sets, we present a perceptron formulation of the Ranking Support Vector Machine algorithm. We present initial results on inferring the rank of images presented in a page based on simple image features and implicit feedback of users. The results show that the perceptron algorithm improves the results, and that fusing eye movements and image histograms gives better rankings to images than either of these features alone.

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Citation

Pasupa, Kitsuchart, Saunders, Craig, Szedmak, Sandor, Klami, Arto, Kaski, Samuel and Gunn, Steve (2009) Learning to Rank Images from Eye Movements At Proceeding of 2009 IEEE 12th International Conference on Computer Vision (ICCV'2009) Workshop on Human-Computer Interaction (HCI'2009), Japan. 27 Sep - 04 Oct 2009. , pp. 2009-2016.

More information

Published date: 4 October 2009
Additional Information: Event Dates: 27 September - 4 October 2009
Venue - Dates: Proceeding of 2009 IEEE 12th International Conference on Computer Vision (ICCV'2009) Workshop on Human-Computer Interaction (HCI'2009), Japan, 2009-09-27 - 2009-10-04
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 267964
URI: http://eprints.soton.ac.uk/id/eprint/267964
PURE UUID: 09b8bf96-eb94-42e9-ab91-14bc11c24e88

Catalogue record

Date deposited: 27 Sep 2009 14:54
Last modified: 18 Jul 2017 06:58

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Contributors

Author: Kitsuchart Pasupa
Author: Craig Saunders
Author: Sandor Szedmak
Author: Arto Klami
Author: Samuel Kaski
Author: Steve Gunn

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