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In this paper we address issues with image retrieval benchmarking on standardand popular Oxford 5k and Paris 6k datasets. In particular, annotation errors,the size of the dataset, and the level of challenge are addressed: newannotation for both datasets is created with an extra attention to thereliability of the ground truth. Three new protocols of varying difficulty areintroduced. The protocols allow fair comparison between different methods,including those using a dataset pre-processing stage. For each dataset, 15 newchallenging 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 thenew benchmark. Different types of methods are evaluated, ranging fromlocal-feature-based to modern CNN based methods. The best results are achievedby taking the best of the two worlds. Most importantly, image retrieval appearsfar from being solved.

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking