Quantifying contextual information for object detection

Wei-Shi Zheng, Shaogang Gong, Tao Xiang

Abstract: Context is critical for minimising ambiguity in object detection. In this work, a novel context modelling framework is proposed without the need of any prior scene segmentation or context annotation. This is achieved by exploring a new polar geometric histogram descriptor for context representation. In order to quantify context, we formulate a new context risk function and a maximum margin context (MMC) model to solve the minimization problem of the risk function. Crucially, the usefulness and goodness of contextual information is evaluated directly and explicitly through a discriminant context inference method and a context confidence function, so that only reliable contextual information that is relevant to object detection is utilised. Experiments on PASCAL VOC2005 and i-LIDS datasets demonstrate that the proposed context modelling approach improves object detection significantly and outperforms a state-of-the-art alternative context model.