Conference Publication Details
Mandatory Fields
Barbu A.;Barrett D.;Chen W.;Siddharth N.;Xiong C.;Corso J.;Fellbaum C.;Hanson C.;Hanson S.;Hélie S.;Malaia E.;Pearlmutter B.;Siskind J.;Talavage T.;Wilbur R.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Seeing is worse than believing: Reading people's minds better than computer-vision methods recognize actions
Optional Fields
action recognition fMRI
We had human subjects perform a one-out-of-six class action recognition task from video stimuli while undergoing functional magnetic resonance imaging (fMRI). Support-vector machines (SVMs) were trained on the recovered brain scans to classify actions observed during imaging, yielding average classification accuracy of 69.73% when tested on scans from the same subject and of 34.80% when tested on scans from different subjects. An apples-to-apples comparison was performed with all publicly available software that implements state-of-the-art action recognition on the same video corpus with the same cross-validation regimen and same partitioning into training and test sets, yielding classification accuracies between 31.25% and 52.34%. This indicates that one can read people's minds better than state-of-the-art computer-vision methods can perform action recognition. © 2014 Springer International Publishing.
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