Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
原文地址：Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
摘要
In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth. Three new protocols of varying difficulty are introduced. The protocols allow fair comparison between different methods, including those using a dataset pre-processing stage. For each dataset, 15 new challenging queries are introduced. Finally, a new set of 1M hard, semi-automatically cleaned distractors is selected.
An extensive comparison of the state-of-the-art methods is performed on the new benchmark. Different types of methods are evaluated, ranging from local-feature-based to modern CNN based methods. The best results are achieved by taking the best of the two worlds. Most importantly, image retrieval appears far from being solved.
本文解决了数据集Oxford 5k和Paris 6k上存在的关于图像检索基准的问题。针对标注错误、数据集大小以及挑战等级都进行了调整：
- 重新标注了两个数据集，提高了真值标签的可靠性；
- 通过变化检索难度得到三个基准数据集，这样可以更公平的比较不同的方法，包括对数据集进行了预处理的算法；
- 每个数据集增加了15张新的具有挑战性的查询图像，以及通过半自动清洗得到的1M干扰图像。
在新的基准上详细比较了当前最先进的方法，从基于局部特征的方法到基于CNN的方法。从结果来看，图像检索任务似乎远远没有解决。