  An automatic mouse behavior recognition system can considerably reduce the
workload of experimenters and facilitate the analysis process. Typically,
supervised approaches, unsupervised approaches and semi-supervised approaches
are applied for behavior recognition purpose under a setting which has all of
predefined behaviors. In the real situation, however, as mouses can show
various types of behaviors, besides the predefined behaviors that we want to
analyze, there are many undefined behaviors existing. Both supervised
approaches and conventional semi-supervised approaches cannot identify these
undefined behaviors. Though unsupervised approaches can detect these undefined
behaviors, a post-hoc labeling is needed. In this paper, we propose a
semi-supervised infinite Gaussian mixture model (SsIGMM), to incorporate both
labeled and unlabelled information in learning process while considering
undefined behaviors. It also generates the distribution of the predefined and
undefined behaviors by mixture Gaussians, which can be used for further
analysis. In our experiments, we confirmed the superiority of SsIGMM for
segmenting and labelling mouse-behavior videos.
