keras_rmac_plus

Keras implementation of R-MAC+ descriptors.

[paper] [project]

The image below represents the query phase exeucted for the R-MAC+ descriptors.

Prerequisites for Python3

• Keras (> 2.0.0)
• Tensorflow (> 1.5)
• Scipy
• Sklearn
• OpenCV 3

Networks

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.

Datasets

• Holidays
• Oxford5k
• Paris6k

Download the datasets and put it into the data folder. Then compile the script for the evaluation of the retrieval system.

Test

python3 Keras_test_MAC.py

Results

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.

References

@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}
}