Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00325470" target="_blank" >RIV/68407700:21230/18:00325470 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/~radenfil/publications/Radenovic-CVPR18.pdf" target="_blank" >http://cmp.felk.cvut.cz/~radenfil/publications/Radenovic-CVPR18.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR.2018.00598" target="_blank" >10.1109/CVPR.2018.00598</a>
Alternative languages
Result language
angličtina
Original language name
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CVPR 2018: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition
ISBN
978-1-5386-6420-9
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
5706-5715
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Salt Lake City
Event date
Jun 19, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000457843605089