Re-Identification of Zebrafish using Metric Learning

Zebrafish are widely used for drug development and behavioral pattern studies. The currently employed zebrafish re-identification methods rely solely on top-view and grayscale images which require a significant amount of annotated data in order to perform well. In this paper, for the first time, we perform zebrafish re-identification using RGB images recorded from a side-view perspective, while keeping the amount of data annotation to a minimum. Inspired by the person re-identification field, two feature descriptors are tested, each encoding both color and texture information, and five metric and subspace learning methods. The contribution of the color and texture components of the feature descriptors were also investigated. We present and evaluate on a novel publicly available dataset of six zebrafish, recorded in a laboratory setup. The results show that a mean average precision of 99% can be achieved by using just 15 annotated samples per fish. This approach shows a clear potential for incorporating the side-view information in the field of zebrafish tracking, as well as a clear argument for utilizing the color information of the zebrafish.