NDLI: Novel image classification based on decision-level fusion of EEG and visual features

Content Provider	IEEE Xplore Digital Library
Author	Kawakami, T. Ogawa, T. Haseyama, M.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan (Kawakami, T.; Ogawa, T.; Haseyama, M.)
Abstract	This paper presents a novel image classification based on decision-level fusion of EEG and visual features. In the proposed method, we extract the EEG features from EEG signals recorded while users stare at images, and the visual features are computed from these images. Then the classification of images is performed based on Support Vector Machine (SVM) by separately using the EEG and visual features. Furthermore, we merge the above classification results based on Supervised Learning from Multiple Experts to obtain the final classification result. This method focuses on the classification accuracy calculated from each classification result. Therefore, although classification accuracy based on EEG and visual features are different from each other, our method realizes effective integration of these classification results. In addition, we newly derive a kernelized version of the method in order to realize more accurate integration of the classification results. Consequently, our method realizes successful multimodal classification of images by the object categories that they contain.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	5874
Ending Page	5878
File Size	98269
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Visualization Feature extraction Accuracy Vectors Image segmentation Training data decision-level fusion electroencephalogram image classification multimodal scheme
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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