Seabed Classification Using a Bag-of-Prototypes Feature Representation

The increasing scientific and economic interest in the visual exploration and monitoring of marine areas is creating huge amounts of new underwater image and video data and approaches to computationally assisted analysis are desperately needed. In this paper we propose an image patch feature representation concept, the Bag of Prototypes (BoP), to cope with the individual problems in underwater image analysis. We consider the case of seafloor classification, which is relevant in many contexts such as habitat mapping or the exploration of mineral resources and show, that the BoP concept allows an efficient and accurate tile-wise estimation of poly-metallic nodule coverage in relation to two differently acquired gold standards.