Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
This work provides a more reliable benchmark for image retrieval researchers, though it is incremental as it refines existing datasets rather than introducing new methods.
The paper addresses issues in image retrieval benchmarking by creating new annotations, protocols, and distractors for Oxford and Paris datasets, and finds that combining local-feature-based and CNN-based methods yields the best results, though image retrieval remains unsolved.
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.