Discovering spatial context prototypes for object detection

Contextual information is widely exploited in the-state-of-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 of objects manifests heterogeneous statistical distribution for different object classes, which suggests mining class-specified prototypes of spatial contexts in a data-driven manner. This paper proposes a novel clustering-based method for automatically discovering spatial context prototypes to beyond the pre-defined spatial relationship representation in literature. Based on the learned prototypes, we further construct a compact representation on spatial context feature, by means of efficient coding method of soft-assignment quantization. Our experimental results on PASCAL VOC dataset demonstrate that the proposed method can capture meaningful spatial context prototypes for various object class pairs and thus boost recognition performance on object detection task.