Metric learning based automatic segmentation of patterned species

Many species in the wild exhibit a visual pattern that can be used to uniquely identify an individual. This observation has recently led to visual animal biometrics become a rapidly growing application area of computer vision. Customized software tools for animal biometrics already employ vision based techniques to recognize individuals in images taken in uncontrolled environments. However, most existing tools require the user to localize the animals for accurate identification. In this work, we propose a figure/ground segmentation method that automatically extracts out the animal in an image. Our method relies on a semi-supervised metric learning algorithm that uses a small amount of training data without compromising generalization performance. We design a simple pipeline comprising of superpixel segmentation, texture based feature extraction followed by mean shift clustering using the learned metric. We show that our approach can yield competitive results for figure/ground segmentation of patterned animals in images taken in the wild, often under extreme illumination conditions.