Fusion of spatially constrained attributes with kernelized ranking for person re-identification

The task of matching persons across non-overlapping camera views, known as person re-identification, is rather challenging due to strong visual similarity and large appearance changes caused by illumination, pose and occlusion. Most approaches rely on low-level features that are both discriminative and invariant. In this work, we propose a novel method to address this problem by fusing mid-level semantic attributes with kernelized ranking. First, a kernelized ranking model is learned, and it gives the initial ranking scores. Next, an adaptive similarity model based on spatially constrained attributes is used to refine the ranking list. Fusion of the two models leads to much better performance than each individual alone. Experiments demonstrate complements of the two models and the results achieve new state-of-the-art performance on two benchmark datasets.