Statistical learning applied to computer-assisted fish age and growth estimation from otolith images

Computer-assisted tools need to be developed to help in the accurate and efficient acquisition of fish age and growth data for ecological and assessment issues. Stating fish age and growth analysis as pattern classification issues, the proposed approach relies on a statistical learning strategy. Given otolith images interpreted by an expert, probabilistic kernel-based methods (namely Kernel Logistic Regression) are used to infer interpretation rules. More precisely, two different probabilistic models are introduced: one to infer fish age from otolith images and a second one aiming at evaluating whether or not a given otolith growth pattern is realistic w.r.t. training examples. These probabilistic models provide us with the basis for coping with three different issues: the automated estimation of fish age from otolith images, the estimation of individual otolith growth patterns, and the definition of a confidence measure of otolith interpretations. These computer-assisted ageing tools are validated for a dataset of plaice otoliths.