Weakly supervised classification with bagging in fisheries acoustics

Statistical training allows the establishment of a probabilistic classification model. In the supervised case, the model is assessed from a labelled dataset, i.e. each observed data has a label. In the weakly-supervised case, the label is not exactly known. In our instance, the probability to associate the observation to the different classes is known. Thus, labels for the data are a probability vector. Methods developed in this paper are applied to object recognition in images. These images contain objects that must be classified according to their class membership. The ground truth is the knowledge of the relative proportion of classes in each labelled images. This global proportion leads to probability vector label for each training object. The originality of this paper consists in the association between weakly labelled data and several probabilistic discriminative models that are mixed using a bagging technique. Two classification models (Bayesian and discriminative) are compared on oceanographic data. The objective is to recognize the species of fish schools in acoustic images. The relative class proportion in labelled images is given by successive trawl catches. The results show that the discriminative model is more robust than the Bayesian model. The contribution of the bagging is shown for the discriminative model.