Predicting relevance of parts of an image
Predicting relevance of parts of an image
This report studies the task of inferring which parts of an image are relevant for the user viewing the image. The relevance is inferred from gaze trajectory of users viewing the images given a specific task. Novel computational models based on both Bayesian generative modeling and kernel methods are developed for inferring the regions of interest from raw fixation data, as well as from combination of eye movements and image content features.
Klami, Arto
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Kaski, Samuel
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Pasupa, Kitsuchart
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Szedmak, Sandor
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Gunn, Steve
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Hardoon, David
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Csurka, Gabriela
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31 December 2009
Klami, Arto
7d24333d-4417-42ba-a188-c22dea625f1d
Kaski, Samuel
87dad6b5-fda3-494f-84f9-1de967bf1458
Pasupa, Kitsuchart
952ededb-8c97-41b7-a65b-6aba31de2669
Szedmak, Sandor
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Gunn, Steve
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Hardoon, David
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Csurka, Gabriela
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Klami, Arto, Kaski, Samuel, Pasupa, Kitsuchart, Szedmak, Sandor, Gunn, Steve, Hardoon, David and Csurka, Gabriela
(2009)
Predicting relevance of parts of an image
Record type:
Monograph
(Project Report)
Abstract
This report studies the task of inferring which parts of an image are relevant for the user viewing the image. The relevance is inferred from gaze trajectory of users viewing the images given a specific task. Novel computational models based on both Bayesian generative modeling and kernel methods are developed for inferring the regions of interest from raw fixation data, as well as from combination of eye movements and image content features.
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Published date: 31 December 2009
Organisations:
Electronic & Software Systems
Identifiers
Local EPrints ID: 268375
URI: http://eprints.soton.ac.uk/id/eprint/268375
PURE UUID: 8eecd4df-3223-4fba-89f2-71a28e19c217
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Date deposited: 13 Jan 2010 09:15
Last modified: 14 Mar 2024 09:09
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Contributors
Author:
Arto Klami
Author:
Samuel Kaski
Author:
Kitsuchart Pasupa
Author:
Sandor Szedmak
Author:
Steve Gunn
Author:
David Hardoon
Author:
Gabriela Csurka
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