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PAMI
2012

Quantifying and Transferring Contextual Information in Object Detection

6 years 6 months ago
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 ...
Wei-Shi Zheng, Shaogang Gong, Tao Xiang
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where PAMI
Authors Wei-Shi Zheng, Shaogang Gong, Tao Xiang
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