  As deep learning approaches to scene recognition emerge, they have continued
to leverage discriminative regions at multiple scales, building on practices
established by conventional image classification research. However, approaches
remain largely generic, and do not carefully consider the special properties of
scenes. In this paper, inspired by the intuitive differences between scenes and
objects, we propose Adi-Red, an adaptive approach to discriminative region
discovery for scene recognition. Adi-Red uses a CNN classifier, which was
pre-trained using only image-level scene labels, to discover discriminative
image regions directly. These regions are then used as a source of features to
perform scene recognition. The use of the CNN classifier makes it possible to
adapt the number of discriminative regions per image using a simple, yet
elegant, threshold, at relatively low computational cost. Experimental results
on the scene recognition benchmark dataset SUN397 demonstrate the ability of
Adi-Red to outperform the state of the art. Additional experimental analysis on
the Places dataset reveals the advantages of Adi-Red, and highlight how they
are specific to scenes. We attribute the effectiveness of Adi-Red to the
ability of adaptive region discovery to avoid introducing noise, while also not
missing out on important information.
