The Animal ID Problem: Continual Curation
This addresses the challenge of open-world recognition for animal identification, which is incremental as it builds on existing methods but introduces a new curation framework.
The paper tackles the problem of individual animal identification from images by framing it as a Continual Curation task, which involves improving recognition methods, developing verification algorithms with no-decision options, and using human input to guide curation, with performance measured by accuracy relative to human input required.
Hoping to stimulate new research in individual animal identification from images, we propose to formulate the problem as the human-machine Continual Curation of images and animal identities. This is an open world recognition problem, where most new animals enter the system after its algorithms are initially trained and deployed. Continual Curation, as defined here, requires (1) an improvement in the effectiveness of current recognition methods, (2) a pairwise verification algorithm that allows the possibility of no decision, and (3) an algorithmic decision mechanism that seeks human input to guide the curation process. Error metrics must evaluate the ability of recognition algorithms to identify not only animals that have been seen just once or twice but also recognize new animals not in the database. An important measure of overall system performance is accuracy as a function of the amount of human input required.