Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions
Name
CBMM-Memo-012.pdf
Size
678.95 KB
Format
Adobe PDF
Checksum (MD5)
0a75394161b6fe20ca4957cef9e04858
Author(s)
Barbu, Andrei
Barrett, Daniel P.
Chen, Wei
Narayanaswamy, Siddharth
Xiong, Caiming
Corso, Jason J.
Fellbaum, Christiane D.
Hanson, Catherine
Hanson, Stephen Jose
Helie, Sebastien
Date Issued
December 10, 2015
Series/Report no.
CBMM Memo Series;012
Abstract
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.
Subjects
Object Recognition
Vision
Support-Vector Machines (SVMs)
Terms of Use
Attribution-NonCommercial 3.0 United States
Persistent DSpace Link
