Combining computer and human vision into a BCI: can the whole be greater than the sum of its parts?
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference(2010)
摘要
Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.
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关键词
computer vision image retrieval system,single-trial eeg scores,sparse labels,learning (artificial intelligence),sparse matrices,image feature similarity,electroencephalography,brain-computer interfaces,bci systems,biomedical equipment,human vision,user attention,computer vision system,vision,feature extraction,image classification,image retrieval,computer vision,graph-based transductive learning model,triaging image content,medical image processing,brain computer interfaces,detectors,testing,image features,transductive learning,learning artificial intelligence
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