  Scene recognition is a fundamental task in robotic perception. For human
beings, scene recognition is reasonable because they have abundant object
knowledge of the real world. The idea of transferring prior object knowledge
from humans to scene recognition is significant but still less exploited. In
this paper, we propose to utilize meaningful object representations for indoor
scene representation. First, we utilize an improved object model (IOM) as a
baseline that enriches the object knowledge by introducing a scene parsing
algorithm pretrained on the ADE20K dataset with rich object categories related
to the indoor scene. To analyze the object co-occurrences and pairwise object
relations, we formulate the IOM from a Bayesian perspective as the Bayesian
object relation model (BORM). Meanwhile, we incorporate the proposed BORM with
the PlacesCNN model as the combined Bayesian object relation model (CBORM) for
scene recognition and significantly outperforms the state-of-the-art methods on
the reduced Places365 dataset, and SUN RGB-D dataset without retraining,
showing the excellent generalization ability of the proposed method. Code can
be found at https://github.com/hszhoushen/borm.
