Varying resolution quality of operational data, size of targets, view occlusions, and large variation in sensors due to nature of overhead systems as compared to consumer devices contribute to degradation of the maritime vessel identification. We exploit the maritime domain characteristics to optimize and refine the deep learning Mask-RCNN framework for training generic maritime vessel classes. Maritime domain, compared to consumer domain, lack alternative targets that would be incorrectly associated as maritime vehicles: this allows us to relax the parameter constraints learned on urban natural scenes in consumer photos, adjust parameters of the model inference, and achieve robust performance and high AP measure for transfer learning scenarios. In this paper, we build upon this robust localization work, and extend our transfer learning work to new domains and datasets. We propose new approach for identifying specific category of maritime vessels and build a refined multi-label classifier that is based on deep Mask-RCNN features. The classifier is designed to be robust to domain transfer (e.g. different overhead maritime video feed), and to the noise in the data annotation (e.g. vessel is not correctly marked or label is ambiguous). We demonstrate superior category classification results of this low shot learning approach on publicly available MarDCT dataset.
This paper presents an overview of our recent work on managing image and video data. The first half of the paper describes a representation for the semantic spatial layout of video frames. In particular, Markov random fields are used to characterize the spatial arrangement of frame tiles that are labeled using support vector machine classifiers. The representation is shown to support similarity retrieval at the semantic level as demonstrated in a prototype video management system. The second half of the paper describes a method for efficiently computing nearest neighbor queries in high-dimensional feature spaces in a relevance feedback framework.