Towards optimal object bank for scene classification

High-level image representations have drawn increasing attention in visual recognition, e.g., scene classification, since the invention of the object bank (OB). The object bank represents an image as a response map of a large number of pre-trained object detectors and has achieved superior performances for visual recognition. However, the object bank representation can be further improved by considering the distributions of the object across categories and the discriminative contributions to the image representation. In this paper, we propose an optimal object bank (OOB) by imposing weights on the detectors according to their discriminative abilities. Through extensive experiments on two benchmark datasets: UIUC-Sports dataset and 15-Scene dataset, we prove that the proposed OOB can significantly improve the original object bank and achieves state-of-the-art performances.