Indoor Scene Recognition from RGB-D Images by Learning Scene Bases

In this paper, we propose a RGB-D indoor scene recognition method that has mainly two advantages as compared to existing methods. First, by training object detectors using RGB-D images and recognizing their spatial interrelationships, we not only achieve better object localization accuracy than using RGB images alone, but also obtain details as to how the objects are related to each other in a spatial manner, thus resulting in a more effective high-level feature representation of the scene known as the Objects and Attributes (O&A) representation. Second, we learn class-specific sub-dictionaries that capture the high-order couplings between the objects and attributes. In particular, elastic net regularization and geometric similarity constraint is imposed to increase the discriminative power of the sub-dictionaries. The proposed method is evaluated on two RGB-D datasets, the NYUD dataset and the B3DO dataset. Experiments show that superior scene recognition rate can be obtained using our method.