A hierarchical image kernel with application to pedestrian identification for video surveillance

Video surveillance usually requires multiple cameras to monitor objects of interest, such as people. However, different appearances acquired from different cameras of the same people often make the construction of a robust individualized appearance model very challenging. In this paper, we present a kernel-based method that maps the bag-of-feature based image features to a hierarchical representation. The image comparison is performed through summing the weighted similarities of nodes in the hierarchical structure. The kernel is also proven to be positive-definite, making it valid for use in other kernel-based learning algorithms. In the experiments we show the classifier embedded with our kernel function is robust against view-point and scaling variations, and it is more accurate compared to other related approaches.