Keras implementation of R-MAC+ descriptors
Keras implementation of R-MAC+ descriptors.
The image below represents the query phase exeucted for the R-MAC+ descriptors.
The pipeline was tested with VGG16 and ResNet50. For the VGG16 the best performance are reached when the features are extracted from the block5_pool, instead for ResNet from the activation_43.
It is possible to try with other networks. Please before to try it, check if there are available the Keras weight for the selected network.
Download the datasets and put it into the data folder. Then compile the script for the evaluation of the retrieval system.
python3 Keras_test_MAC.py
| Method | Network | Oxford5k | Paris6k | Holidays |
|---|---|---|---|---|
| R-MAC | VGG16 | 65.56% | 82.80% | 87.65% |
| R-MAC | ResNet50 | 71.77% | 83.31% | 92.55% |
| M-R RMAC+ | ResNet50 | 78.88% | 88.63% | 94.63% / 95.58% |
| M-R RMAC+ with retrieval based on ‘db regions’ | ResNet50 | 85.39 % | 91.90% | 94.37% / 95.87% |
The R-MAC is an our re-implementation of the Tolias et al. 2016 paper, instead M-R RMAC comes from the Gordo et al. 2016 paper.
The last two experiments are also executed on the rotated version of Holidays.
- @article{magliani2018accurate,
title={An accurate retrieval through R-MAC+ descriptors for landmark recognition},
author={Magliani, Federico and Prati, Andrea},
journal={arXiv preprint arXiv:1806.08565},
year={2018}
}@article{tolias2015particular,
title={Particular object retrieval with integral max-pooling of CNN activations},
author={Tolias, Giorgos and Sicre, Ronan and J{\’e}gou, Herv{\’e}},
journal={arXiv preprint arXiv:1511.05879},
year={2015}
}@inproceedings{gordo2016deep,
title={Deep image retrieval: Learning global representations for image search},
author={Gordo, Albert and Almaz{\’a}n, Jon and Revaud, Jerome and Larlus, Diane},
booktitle={European Conference on Computer Vision},
pages={241—257},
year={2016},
organization={Springer}
}