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Exploring EEG for Object Detection and Retrieval

Authors:

Xavier Giró-i-NietoAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 591 - 594

https://doi.org/10.1145/2671188.2749368

Published: 22 June 2015 Publication History

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Abstract

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based 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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Cited By

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Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Sharma SDubey ARanjan PRocha A(2023)A deep perceptual framework for affective video tagging through multiband EEG signals modelingNeural Computing and Applications10.1007/s00521-023-09086-8Online publication date: 17-Oct-2023
https://doi.org/10.1007/s00521-023-09086-8
Sharma SDubey ARanjan P(2022)Affective Video Tagging Framework using Human Attention Modelling through EEG SignalsInternational Journal of Intelligent Information Technologies10.4018/IJIIT.30696818:1(1-18)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJIIT.306968
Show More Cited By

Index Terms

Exploring EEG for Object Detection and Retrieval
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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Published In

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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Short-paper

Funding Sources

Science Foundation Ireland

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ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

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Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Sharma SDubey ARanjan PRocha A(2023)A deep perceptual framework for affective video tagging through multiband EEG signals modelingNeural Computing and Applications10.1007/s00521-023-09086-8Online publication date: 17-Oct-2023
https://doi.org/10.1007/s00521-023-09086-8
Sharma SDubey ARanjan P(2022)Affective Video Tagging Framework using Human Attention Modelling through EEG SignalsInternational Journal of Intelligent Information Technologies10.4018/IJIIT.30696818:1(1-18)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJIIT.306968
Sharma SDubey ARanjan PRocha A(2021)Neural correlates of affective content: application to perceptual tagging of videoNeural Computing and Applications10.1007/s00521-021-06591-635:11(7925-7941)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1007/s00521-021-06591-6
Lees SDayan NCecotti HMcCullagh PMaguire LLotte FCoyle D(2018)A review of rapid serial visual presentation-based brain–computer interfacesJournal of Neural Engineering10.1088/1741-2552/aa981715:2(021001)Online publication date: 24-Jan-2018
https://doi.org/10.1088/1741-2552/aa9817

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Object Segmentation in Images using EEG Signals

On the Size of the Universal Dictionaries Used in EEG P300 Spelling Paradigm Based on Compressed Sensing

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