Joint ship classification and learning

This paper proposes a ship recognition system that jointly classifies and learns from unlabeled data within a sparse representation framework. Compact dictionaries based on local descriptors serve as the basis for the classification system via l₁ minimization techniques. Previous research has demonstrated the advantages of exploiting sparsity within the recognition context. Creating a dictionary based on invariant descriptors provides robustness to changes in illumination and to affine transformations. Traditional approaches assume that the training data will span future test samples implying that the training set includes a complete object representation. Such data sets are difficult to obtain and in the end the system´s performance is highly dependent on the training data sets. This framework implements a flexible learning approach where dictionaries can be augmented or updated with relevant data from unlabeled test samples.