Adaptive Metric Learning and Probe-Specific Reranking for Person Reidentification

作者：Xie, Yi; Yu, Huimin^*; Gong, Xiaojin; Levine, Martin D.

来源：IEEE Signal Processing Letters, 2017, 24(6): 853-857.

DOI：10.1109/LSP.2017.2679208

• Hard negative mining
• metric learning
• person reidentification
• reranking
• transductive learning

Inthis letter, we introduce an adaptive metric learning (AML) method for person reidentification. Different from conventional metric learning approaches, which treat all the negative samples equally, AML adaptively classifies the negative samples into three groups and pays different attention to them. By emphasizing the influence of hard negative samples, AML can better mine the discriminative information between positive and negative samples, and thus generate a more effective metric. Furthermore, we also propose a probe-specific reranking (PSR) algorithm to refine the initial ranking list measured by the learned metric. For each probe, PSR constructs a corresponding hypergraph to capture the neighborhood relationship between the probe and its top 100 ranked gallery images. Then, these images are reranked based on their neighborhood affinity in the hypergraph. Extensive experiments on three challenging datasets demonstrate the superiority of both AML and PSR.

• 出版日期2017-6
• 单位浙江大学