面向行人重识别的对应结构学习
Learning Correspondence Structures for Person Re-identification
AI总结：
针对行人重识别中视角与姿态导致的空间失配问题，提出基于boosting的对应结构学习方法，结合全局约束匹配与多结构方案提升匹配可靠性，经多数据集验证有效。
摘要：
本文针对行人重识别中由相机视角变化或人体姿态差异导致的空间失配问题展开研究。首先提出一种基于boosting（提升）的方法，用于学习对应结构，该结构可表征目标相机对下图像间的逐块匹配概率。学习得到的对应结构不仅能捕捉相机间的空间对应模式，还可应对单张图像内的视角或人体姿态变化。本文进一步提出基于全局约束的匹配流程，在学习到的对应结构上引入全局匹配约束，在图像块匹配过程中排除跨视角失配，从而得到更可靠的图像间匹配分数。最后，本文还通过引入多结构方案对方法进行扩展：学习一组局部对应结构以捕捉相机对间的空间对应子模式，从而更精准地处理单张图像间的空间失配。在多个数据集上的实验结果验证了本文方法的有效性。
英文摘要：
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
