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

Centroid Based Concept Learning for RGB-D Indoor Scene Classification