  This paper contributes a novel cognitively-inspired method for RGB-D indoor
scene classification. High intra-class variance and low inter-class variance
make indoor scene classification an extremely challenging task. To cope with
this problem, we propose a clustering approach inspired by the concept learning
model of the hippocampus and the neocortex, to generate clusters and centroids
for different scene categories. Test images depicting different scenes are
classified by using their distance to the closest centroids (concepts).
Modeling of RGB-D scenes as centroids not only leads to state-of-the-art
classification performance on benchmark datasets (SUN RGB-D and NYU Depth V2),
but also offers a method for inspecting and interpreting the space of
centroids. Inspection of the centroids generated by our approach on RGB-D
datasets leads us to propose a method for merging conceptually similar
categories, resulting in improved accuracy for all approaches.
