This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in contentbased image retrieval. Several experiments are performed using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We compare the feedback from the BCI and mouse-based interfaces
in a subset of TRECVid images, finding that, when
users have limited time to annotate the images, both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback can outperform mouse-based feedback.
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Status = Published
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Subject = Engineering: Signal processing
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Subject = Computer Science: Computer engineering
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Dublin City University ->
DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
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Dublin City University ->
Subject = Engineering
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Dublin City University ->
DCU Faculties and Centres = Research Initiatives and Centres
Ireland ->
Dublin City University ->
Publication Type = Conference or Workshop Item
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Subject = Computer Science: Interactive computer systems
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Dublin City University ->
Subject = Computer Science
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Dublin City University ->
Subject = Computer Science: Information retrieval
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Dublin City University ->
Subject = Computer Science: Machine learning
Alan F. Smeaton,
Noel E. O'Connor,
Graham Healy,
Kevin McGuinness,
Xavier Giró-i-Nieto,
Sergi Porta Caubet,
Amaia Salvador,
Eva Mohedano