Person re-identification by improved Local Maximal Occurrence with color names

Person re-identification is the task of associating people across cameras with non-overlapping view field. Two key aspects of Person re-identification are the feature representation and metric learning. The feature representation employed should be both discriminative and invariant, which is also our considering in this paper. To enhance person re-identification performance, we propose to combine improved Local Maximal Occurrence (LOMO) descriptor with semantic color names (SCN). Especially, we introduce symmetry information of human body to suppress the impact of background in LOMO. When fused with mid-level attribute-based description - sematic color names, our more discriminative signature is obtained. Based on the KISS metric, evaluation on the challenging VIPeR dataset shows that the proposed method improves the re-identification significantly.