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.

翻译：我们提出ElasticHash，一种面向高质量、高效且大规模语义图像相似性搜索的新方法。该方法基于深度哈希模型，用于学习自然图像中细粒度图像相似性搜索的哈希编码，并采用两阶段策略借助Elasticsearch（ES）高效检索二进制哈希编码。第一阶段利用短哈希编码通过多索引哈希及ES术语查询相邻哈希码完成粗搜索；第二阶段通过计算长哈希编码的汉明距离对结果列表进行重排序。我们在OpenImages数据集的约690万张图像库中，对超过12万张查询图像评估了ElasticHash的检索性能。结果表明，我们的方法能够实现高质量检索结果与低搜索延迟。