Generic and attribute-specific deep representations for maritime vessels

Fine-grained visual categorization has recently received great attention as the volumes of labeled datasets for classification of specific objects, such as cars, bird species, and air-crafts, have been increasing. The availability of large datasets led to significant performance improvements in several vision-based classification tasks. Visual classification of maritime vessels is another important task, assisting naval security and surveillance applications. We introduced, MARVEL, a large-scale image dataset for maritime vessels, consisting of 2 million user-uploaded images and their various attributes, including vessel identity, type, category, year built, length, and tonnage, collected from a community website. The images were categorized into vessel type classes and also into superclasses defined by combining semantically similar classes, following a semi-automatic clustering scheme. For the analysis of the presented dataset, extensive experiments have been performed, involving several potentially useful applications: vessel type classification, identity verification, retrieval, and identity recognition with and without prior vessel type knowledge. Furthermore, we attempted interesting problems of visual marine surveillance such as predicting and classifying maritime vessel attributes such as length, summer deadweight, draught, and gross tonnage by solely interpreting the visual content in the wild, where no additional cues such as scale, orientation, or location are provided. By utilizing generic and attribute-specific deep representations for maritime vessels, we obtained promising results for the aforementioned applications.