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

TL;DR

This paper introduces R-MAC+ descriptors and a novel retrieval technique using database region descriptors, significantly improving landmark recognition accuracy on multiple datasets.

Contribution

It proposes enhancements to R-MAC descriptors and a new retrieval method leveraging database region descriptors for better landmark recognition.

Findings

Outperforms state-of-the-art on Holidays dataset

Achieves excellent results on Oxford5k and Paris6k datasets

Surpasses previous methods without fine-tuning strategies

Abstract

The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the creation 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 improved. 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.