An accurate retrieval through R-MAC+ descriptors for landmark recognition
This work addresses landmark recognition for computer vision applications, offering incremental improvements over existing methods.
The paper tackled landmark recognition by improving R-MAC descriptors and introducing a novel retrieval technique using 'db regions', resulting in state-of-the-art performance on the Holidays dataset and excellent results on Oxford5k and Paris6k, though outperformed by fine-tuning approaches on some datasets.
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.