This paper introduces a method to automatically identify hidden behavioral patterns in animals using unsupervised machine learning. It leverages k-means clustering and PCA on automatically extracted movement features from dog video trials to overcome human bias and labor-intensive traditional methods. The study successfully applied this to a “Stranger Test,” showing that clusters derived from objective movement features, such as intensity of use and contact ratio, correlated with owner-reported stranger fear and aggression (C-BARQ scores), thus enabling exploratory insights prior to hypothesis formation. However, the results are preliminary due to a small sample size and require further validation.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
