This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is trained on 32x32 patches at keypoint locations. Additionally, we suggest a graph-based reranking algorithm called SGR to exploit similarities of the page embeddings to boost the retrieval performance. Our approach is evaluated on two historical datasets (Historical-WI and HisIR19). We include an evaluation of different backbones and NetRVLAD. It competes with related work on historical datasets without using explicit encodings. We set a new State-of-the-art on both datasets by applying our reranking scheme and show that our approach achieves comparable performance on a modern dataset as well.

翻译：本文提出了一种无监督的作者检索方法，该方法基于对关键点位置检测到的SIFT描述符进行聚类，生成伪聚类标签。利用这些聚类标签，我们训练了一个残差网络，并随后使用本文提出的NetRVLAD（一种相比NetVLAD具有更低复杂度的编码层）对关键点位置的32x32图像块进行训练。此外，我们提出了一种名为SGR的基于图的重排序算法，利用页面嵌入的相似性来提升检索性能。该方法在两个历史数据集（Historical-WI和HisIR19）上进行了评估。我们评估了不同骨干网络和NetRVLAD的性能。在不使用显式编码的情况下，该方法与历史数据集上的相关工作相比具有竞争力。通过应用我们的重排序方案，我们在两个数据集上均取得了新的最优结果，并进一步证明该方法在现代数据集上也能达到可比的性能。