论文标题
使用暹罗神经网络结合全球和本地功能的基于内容的具有里程碑意义的检索
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks
论文作者
论文摘要
在这项工作中，我们提出了一种利用全球和本地功能的地标取回方法。暹罗网络用于全球功能提取和度量学习，该网络给出了地标搜索的初始排名。我们利用暹罗体系结构的提取特征图作为本地描述符，然后使用本地描述符之间的余弦相似性进一步完善搜索结果。我们对Google Landmark数据集进行了更深入的分析，该数据集用于评估，并增加数据集以处理各种类内差异。此外，我们进行了几项实验，以比较转移学习和度量学习的效果，以及使用其他局部描述符的实验。我们表明，使用本地功能的重新排列可以改善搜索结果。我们认为，使用余弦相似性提出的本地特征提取是一种简单的方法，可以扩展到许多其他检索任务。
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.
