A local appearance contextual descriptor for object matching

We present a novel approach to measuring similarity between objects based on matching local “appearance contextual descriptor”. The descriptor has two components: Histogram of Oriented Gradient feature representing local patch appearance and the contextual descriptor capturing not only the spatial distribution of the non-reference patches relative to the reference patch but also the appearance similarities between the reference patch and the non-reference patches in the region. Corresponding patches within two similar objects will have similar contextual descriptors, though the patch appearances may have some difference. We treat recognition in a nearest-neighbor classification framework and match object in regions with no prior learning. We compare our method to commonly used methods and demonstrate its applicability to object detection and recognition.