Object category recognition, in remote sensing imagery, usually relies on exemplar-based training. The latter is achieved by modeling intricate relationships between object categories and visual features. However, for real-world and fine grained object categories - exhibiting complex visual appearance and strong variability - these models may fail especially when training data are scarce. In this paper, we introduce an effective object category recognition approach that alleviates the limitation caused by small training sets. The method learns discriminant mid-level representations (a.k.a. attributes) through nonlinear mappings that make these attributes highly discriminant while being easily trainable and predictable. We demonstrate the effectiveness of our method, through extensive experiments on the challenging task of ship recognition in maritime environments, and we show how our attribute learning model generalizes well in spite of the scarcity of training data.
['Quentin Oliveau', 'Hichem Sahbi']
Attribute learning for ship category recognition in remote sensing imagery