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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.