Trainable, vision-based automated home cage behavioral phenotyping
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MBabstract_NicholasEdelman-1.pdf
Description
author's final submitted manuscript
Size
268.92 KB
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Author(s)
Jhuang, Hueihan
Garrote, Estibaliz
Edelman, Nicholas
Poggio, Tomaso A
Steele, Andrew
Serre, Thomas J.
Date Issued
August 2010
Journal
Measuring Behavior '10: selected papers from the proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research, Article No. 33
Publisher
Association for Computing Machinery
Citation
Jhuang, Hueihan et al. “Trainable, Vision-based Automated Home Cage Behavioral Phenotyping.” in Measuring Behavior '10: Selected papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research, Article No. 33, ACM Press, 2010. 1–4. Web.
Version
Author's final manuscript
Abstract
We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors for training and testing the system. Our system performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home cage behaviors of two standard inbred and two nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.
MIT Department
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1931344.1931377
