Discriminatively-learned global image representation using CNN as a local feature extractor for image retrieval

Paper Pipeline and Key Idea

Take a look at the following pipeline and please refer to paper for more detail.

Paper Citation

Please cite the following if you find this work helpful.

@inproceedings{DBLP:conf/vcip/KuCP15,
  author    = {Wei{-}Lin Ku and
               Hung{-}Chun Chou and
               Wen{-}Hsiao Peng},
  title     = {Discriminatively-learned global image representation using {CNN} as
               a local feature extractor for image retrieval},
  booktitle = {2015 Visual Communications and Image Processing, {VCIP} 2015, Singapore,
               December 13-16, 2015},
  pages     = {1--4},
  year      = {2015},
  crossref  = {DBLP:conf/vcip/2015},
  url       = {https://doi.org/10.1109/VCIP.2015.7457829},
  doi       = {10.1109/VCIP.2015.7457829},
}

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}