An accurate retrieval through R-MAC+ descriptors for landmark recognition

Federico Magliani, Andrea Prati

Abstract

The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Con- volutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the cre- ation of R-MAC descriptors in order to make the newly-proposed R-MAC+ descriptors more representative than the previous ones. However, the main contribution of this paper is a novel retrieval technique, that exploits the fine representativeness of the MAC descriptors of the database images. Using this descriptors called “db regions” during the retrieval stage, the performance is greatly im- proved. The proposed method is tested on different public datasets: Oxford5k, Paris6k and Holidays. It outperforms the state-of-the- art results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies.

Paper

Preprint PDF: An accurate retrieval through R-MAC+ descriptors for landmark recognition

@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}
}

Slides