  Accurate perception of the surrounding scene is helpful for robots to make
reasonable judgments and behaviours. Therefore, developing effective scene
representation and recognition methods are of significant importance in
robotics. Currently, a large body of research focuses on developing novel
auxiliary features and networks to improve indoor scene recognition ability.
However, few of them focus on directly constructing object features and
relations for indoor scene recognition. In this paper, we analyze the
weaknesses of current methods and propose an Object-to-Scene (OTS) method,
which extracts object features and learns object relations to recognize indoor
scenes. The proposed OTS first extracts object features based on the
segmentation network and the proposed object feature aggregation module (OFAM).
Afterwards, the object relations are calculated and the scene representation is
constructed based on the proposed object attention module (OAM) and global
relation aggregation module (GRAM). The final results in this work show that
OTS successfully extracts object features and learns object relations from the
segmentation network. Moreover, OTS outperforms the state-of-the-art methods by
more than 2\% on indoor scene recognition without using any additional streams.
Code is publicly available at: https://github.com/FreeformRobotics/OTS.
