IEEE Trans Pattern Anal Mach Intell. 2021 Mar;43(3):753-765. doi: 10.1109/TPAMI.2019.2944597. Epub 2021 Feb 4.
Image matching and retrieval is the underlying problem in various directions of computer vision research, such as image search, biometrics, and person re-identification. The problem involves searching for the closest match to a query image in a database of images. This work presents a method for generating a consensus amongst multiple algorithms for image matching and retrieval. The proposed algorithm, Shortest Hamiltonian Path Estimation (SHaPE), maps the process of ranking candidates based on a set of scores to a graph-theoretic problem. This mapping is extended to incorporate results from multiple sets of scores obtained from different matching algorithms. The problem of consensus-based decision-making is solved by searching for a suitable path in the graph under specified constraints using a two-step process. First, a greedy algorithm is employed to generate an approximate solution. In the second step, the graph is extended and the problem is solved by applying Ant Colony Optimization. Experiments are performed for image search and person re-identification to illustrate the efficiency of SHaPE in image matching and retrieval. Although SHaPE is presented in the context of image retrieval, it can be applied, in general, to any problem involving the ranking of candidates based on multiple sets of scores.
图像匹配和检索是计算机视觉研究各个方向的基础问题，如图像搜索、生物识别和人员再识别。该问题涉及在图像数据库中搜索与查询图像最接近的匹配。这项工作提出了一种在多种图像匹配和检索算法之间生成共识的方法。所提出的算法，最短哈密顿路径估计（SHaPE），将基于一组分数对候选者进行排名的过程映射到图论问题。这种映射被扩展到包含来自不同匹配算法的多组分数的结果。通过在指定约束下搜索图中的合适路径，使用两步过程解决基于共识的决策问题。首先，使用贪婪算法生成近似解。在第二步中，扩展图并通过应用蚁群优化来解决问题。进行图像搜索和人员再识别的实验，以说明 SHaPE 在图像匹配和检索中的效率。虽然 SHaPE 是在图像检索的背景下提出的，但它通常可以应用于任何涉及基于多组分数对候选者进行排名的问题。
