  Nowadays it is prevalent to take features extracted from pre-trained deep
learning models as image representations which have achieved promising
classification performance. Existing methods usually consider either
object-based features or scene-based features only. However, both types of
features are important for complex images like scene images, as they can
complement each other. In this paper, we propose a novel type of features --
hybrid deep features, for scene images. Specifically, we exploit both
object-based and scene-based features at two levels: part image level (i.e.,
parts of an image) and whole image level (i.e., a whole image), which produces
a total number of four types of deep features. Regarding the part image level,
we also propose two new slicing techniques to extract part based features.
Finally, we aggregate these four types of deep features via the concatenation
operator. We demonstrate the effectiveness of our hybrid deep features on three
commonly used scene datasets (MIT-67, Scene-15, and Event-8), in terms of the
scene image classification task. Extensive comparisons show that our introduced
features can produce state-of-the-art classification accuracies which are more
consistent and stable than the results of existing features across all
datasets.
