title: JAABA: interactive machine learning for automatic annotation of animal behavior.
abstract: We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.
authors.affiliation: Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.
authors.forename: Mayank
authors.initials: M
authors.lastname: Kabra
authors.forename: Alice A
authors.initials: AA
authors.lastname: Robie
authors.forename: Marta
authors.initials: M
authors.lastname: Rivera-Alba
authors.forename: Steven
authors.initials: S
authors.lastname: Branson
authors.forename: Kristin
authors.initials: K
authors.lastname: Branson
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001522:Behavior, Animal; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004331:Drosophila melanogaster / Q000254:growth & development*; D007814:Larva / Q000254:growth & development*; D051379:Mice