Person re-identification suffers from some problems such as confusion with excessive background, incomplete targets, and different viewing point etc. There are two basic observations for most person re-identification applications, i.e., strong correlation exists between the discriminative features, and weak correlation exist between feature points of the pedestrian areas and the background areas. Based on such observations, a person re-identification method based on features correlation is proposed in this paper. The CIP (contextual information processing) module based on viewpoint invariance and soft-detection is applied to realize the proposed method. Since strong correlative features distribute at different granularities, the multi-granularity based person re-identification methods can describe relationship between granularities by embedding the CIP module. The experiments are implemented on four large-scale person re-identification data sets. Both single-domain and cross-domain tests are used in the experiments. The Rank-1 and mean average error criterion are used as the evaluation indicators. As the experiment shows, the proposed method enhances the identification performance of several mainstream multi-granularity methods by CIP module.