In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification. In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations. These correspondence representations are fused to perform the re-identification task. Further, the proposed framework is optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate.

翻译：在这项工作中,我们根据语义成分和色质分布的结合,展示了多气道深层革命金字塔人匹配网络(MC-PPMN),以解决人重新认同问题,特别是,我们从两张个人图像中学习了对语义成分和色质分布的不同深度表达,然后用金字塔人匹配网络(PPMN)获取通信陈述,这些通信表述结合了执行重新认同任务。此外,通过统一的端至端深层学习计划优化了拟议框架。关于几个基准数据集的广泛实验表明,我们应对最新文献,特别是排名一的识别率采取的方法是有效的。