  Previous methods for representing scene images based on deep learning
primarily consider either the foreground or background information as the
discriminating clues for the classification task. However, scene images also
require additional information (hybrid) to cope with the inter-class similarity
and intra-class variation problems. In this paper, we propose to use hybrid
features in addition to foreground and background features to represent scene
images. We suppose that these three types of information could jointly help to
represent scene image more accurately. To this end, we adopt three VGG-16
architectures pre-trained on ImageNet, Places, and Hybrid (both ImageNet and
Places) datasets for the corresponding extraction of foreground, background and
hybrid information. All these three types of deep features are further
aggregated to achieve our final features for the representation of scene
images. Extensive experiments on two large benchmark scene datasets (MIT-67 and
SUN-397) show that our method produces the state-of-the-art classification
performance.
