Centroid Based Concept Learning for RGB-D Indoor Scene Classification

Understand

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).