Discriminative high-level representations for scene classification
High-level image representations, e.g, Object Bank, have drawn increasing attention in visual recognition. In this paper, we propose a discriminative high-level representation based on object bank for scene classification. By projecting the high-level features from the object bank into discriminative subspaces, which are obtained by clustering the features in a supervised way, the final representations are more compact and discriminative. We have conducted extensive experiments on two benchmark datasets: UIUC-Sports dataset and 15-Scene dataset, which demonstrates that the proposed approach can significantly improve the original object bank and achieves state-of-the-art performances.