Contextual Object Detection With Spatial Context Prototypes.

Contextual information is widely exploited in state-of-the-art object detection systems, most of which utilize pre-defined spatial relationships (e.g., above, below, next to, etc.). However, we observe that the spatial arrangement manifests heterogeneous statistical distributions for different object class pairs, which suggests mining class-specified prototypes of spatial contexts in a data-driven manner. This paper proposes a novel contrast K-Means clustering algorithm for automatically discovering spatial context prototypes to beyond the pre-defined spatial relationship representation in literature. Based on the learned prototypes, we further construct the spatial context features by using a simple localized soft assignment quantization method. Besides, considering the large number of real object categories that might lead to overcomplicated spatial context features, we propose a feature refinement method based on the number of context occurrences and K-L divergence to efficiently reduce the complexity of our contextual model. The experiment results on PASCAL VOC dataset and SUN 09 dataset demonstrate that our method can effectively capture meaningful spatial context prototypes as well as most contributing contextual features for different object class pairs and thus boost recognition performance on object detection task.