Hierarchical classification with reject option for live fish recognition

Abstract

A live fish recognition system is needed in application scenarios where manual annotation is too expensive, i.e. too many underwater videos. We present a novel balance-enforced optimized tree with reject option (BEOTR) for live fish recognition. It recognizes the top 15 common species of fish and detects new species in an unrestricted natural environment recorded by underwater cameras. The three main contributions of the paper are: (1) a novel hierarchical classification method suited for greatly unbalanced classes, (2) a novel classification-rejection method to clear up decisions and reject unknown classes, (3) an application of the classification method to free swimming fish. This system assists ecological surveillance research, e.g. fish population statistics in the open sea. BEOTR is automatically constructed based on inter-class similarities. Afterwards, trajectory voting is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. We apply a Gaussian mixture model and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. The proposed BEOTR-based hierarchical classification method achieves significant improvements compared to state-of-the-art techniques on a live fish image dataset of 24,150 manually labelled images from South Taiwan Sea.