  Exploring the semantic context in scene images is essential for indoor scene
recognition. However, due to the diverse intra-class spatial layouts and the
coexisting inter-class objects, modeling contextual relationships to adapt
various image characteristics is a great challenge. Existing contextual
modeling methods for scene recognition exhibit two limitations: 1) They
typically model only one type of spatial relationship (order or metric) among
objects within scenes, with limited exploration of diverse spatial layouts. 2)
They often overlook the differences in coexisting objects across different
scenes, suppressing scene recognition performance. To overcome these
limitations, we propose SpaCoNet, which simultaneously models Spatial relation
and Co-occurrence of objects guided by semantic segmentation. Firstly, the
Semantic Spatial Relation Module (SSRM) is constructed to model scene spatial
features. With the help of semantic segmentation, this module decouples spatial
information from the scene image and thoroughly explores all spatial
relationships among objects in an end-to-end manner, thereby obtaining
semantic-based spatial features. Secondly, both spatial features from the SSRM
and deep features from the Image Feature Extraction Module are allocated to
each object, so as to distinguish the coexisting object across different
scenes. Finally, utilizing the discriminative features above, we design a
Global-Local Dependency Module to explore the long-range co-occurrence among
objects, and further generate a semantic-guided feature representation for
indoor scene recognition. Experimental results on three widely used scene
datasets demonstrate the effectiveness and generality of the proposed method.
