Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks
This work addresses landmark retrieval for computer vision applications, but it is incremental as it builds on existing methods with minor enhancements.
The authors tackled landmark retrieval by combining global and local features using Siamese neural networks, showing that re-ranking with local features improves search results on the Google Landmark Dataset.
In this work, we present a method for landmark retrieval that utilizes global and local features. A Siamese network is used for global feature extraction and metric learning, which gives an initial ranking of the landmark search. We utilize the extracted feature maps from the Siamese architecture as local descriptors, the search results are then further refined using a cosine similarity between local descriptors. We conduct a deeper analysis of the Google Landmark Dataset, which is used for evaluation, and augment the dataset to handle various intra-class variances. Furthermore, we conduct several experiments to compare the effects of transfer learning and metric learning, as well as experiments using other local descriptors. We show that a re-ranking using local features can improve the search results. We believe that the proposed local feature extraction using cosine similarity is a simple approach that can be extended to many other retrieval tasks.