ElasticHash: 基于深度哈希与Elasticsearch的语义图像相似性搜索
ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch
AI总结：
提出ElasticHash，结合深度哈希与Elasticsearch的两阶段搜索，在OpenImages上实现高质量、低延迟的大规模图像相似性检索。
摘要：
我们提出了ElasticHash，一种用于高质量、高效且大规模语义图像相似性搜索的新方法。它基于深度哈希模型，学习自然图像中细粒度图像相似性搜索的哈希码，并采用两阶段方法利用Elasticsearch（ES）高效搜索二进制哈希码。第一阶段，基于短哈希码进行粗搜索，使用多索引哈希和ES对邻近哈希码的术语查找。第二阶段，通过计算长哈希码上的汉明距离对结果列表进行重新排序。我们在OpenImages数据集的约690万张数据库图像上，对超过12万张查询图像评估了ElasticHash的检索性能。结果表明，我们的方法实现了高质量的检索结果和低搜索延迟。
英文摘要：
We present ElasticHash, a novel approach for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES). In the first stage, a coarse search based on short hash codes is performed using multi-index hashing and ES terms lookup of neighboring hash codes. In the second stage, the list of results is re-ranked by computing the Hamming distance on long hash codes. We evaluate the retrieval performance of \textit{ElasticHash} for more than 120,000 query images on about 6.9 million database images of the OpenImages data set. The results show that our approach achieves high-quality retrieval results and low search latencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
