Learning patch-dependent kernel forest for person re-identification

In this paper, we propose a new approach for the person re-identification problem, discovering the correct matches for a query pedestrian image from a set of gallery images. It is well motivated by our observation that the overall complex inter-camera transformation, caused by the change of camera viewpoints, person poses and view illuminations, can be effectively modelled by a combination of many simple local transforms, which guides us to learn a set of more specific local metrics other than a fixed metric working on the feature vector of a whole image. Given training images in pair, we first align the local patches using spatially constrained dense matching. Then, we use a decision tree structure to partition the space of the aligned local patch-pairs into different configurations according to the similarity of the local cross-view transforms. Finally, a local metric kernel is learned for each configuration at the tree leaf nodes in a linear regression manner. The pairwise distance between a query image and a gallery image is summarized based on all the pairwise distance of local patches measured by different local metric kernels. Multiple decision trees form the proposed random kernel forest, which always discriminatively assign the optimal local metric kernel to the local image patches in re-identification. Experimental results over the public benchmarks demonstrate the effectiveness of our approach for achieving very competitive performances with a relatively simpler learning scheme.