Quantifying and Transferring Contextual Information in Object Detection

— Context is critical for reducing the uncertainty in object detection. However, context modelling is challenging because there are often many different types of contextual information co-existing with different degrees of relevance to the detection of target object(s) in different images. It is therefore crucial to devise a context model to automatically quantify and select the most effective contextual information for assisting in detecting the target object. Nevertheless, the diversity of contextual information means that learning a robust context model requires a larger training set than learning the target object appearance model, which may not be available in practice. In this work, a novel context modelling framework is proposed without the need for any prior scene segmentation or context annotation. We formulate a polar geometric context descriptor for representing multiple types of contextual information. In order to quantify context, we propose a new maximum margin context ...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Wei-Shi Zheng, Shaogang Gong, Tao Xiang
• Keywords: Inference Method, PAMI 2012, Software Engineering, Target Object, Target Objects