  Despite the remarkable success of convolutional neural networks in various
computer vision tasks, recognizing indoor scenes still presents a significant
challenge due to their complex composition. Consequently, effectively
leveraging semantic information in the scene has been a key issue in advancing
indoor scene recognition. Unfortunately, the accuracy of semantic segmentation
has limited the effectiveness of existing approaches for leveraging semantic
information. As a result, many of these approaches remain at the stage of
auxiliary labeling or co-occurrence statistics, with few exploring the
contextual relationships between the semantic elements directly within the
scene. In this paper, we propose the Semantic Region Relationship Model (SRRM),
which starts directly from the semantic information inside the scene.
Specifically, SRRM adopts an adaptive and efficient approach to mitigate the
negative impact of semantic ambiguity and then models the semantic region
relationship to perform scene recognition. Additionally, to more
comprehensively exploit the information contained in the scene, we combine the
proposed SRRM with the PlacesCNN module to create the Combined Semantic Region
Relation Model (CSRRM), and propose a novel information combining approach to
effectively explore the complementary contents between them. CSRRM
significantly outperforms the SOTA methods on the MIT Indoor 67, reduced
Places365 dataset, and SUN RGB-D without retraining. The code is available at:
https://github.com/ChuanxinSong/SRRM
