Object recognition using proportion-based prior information: Application to fisheries acoustics

This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning methods for training datasets consisting of groups of objects with known relative class priors. This can be regarded as a generalization of the situation addressed by Bishop and Ulusoy (2005), where training information is given as the presence or absence of object classes in each set. Generative and discriminative classification methods are conceived and compared for weakly supervised learning, as well as a non-linear version of the probabilistic discriminative models. The considered models are evaluated on standard datasets and an application to fisheries acoustics is reported. The proposed proportion-based training is demonstrated to outperform model learning based on presence/absence information and the potential of the non-linear discriminative model is shown.