Detecting Unknown Behaviors by Pre-defined Behaviours: An Bayesian Non-parametric Approach
This work addresses a domain-specific challenge in behavioral neuroscience by enabling automatic detection of unknown mouse behaviors, which is incremental as it builds on semi-supervised and non-parametric approaches.
The paper tackles the problem of recognizing undefined mouse behaviors in video analysis by proposing a semi-supervised infinite Gaussian mixture model (SsIGMM), which outperforms existing methods in segmenting and labeling mouse-behavior videos.
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.