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.

ElasticHash 是一种适用于自然图像的高品质、高效、大规模语义相似性搜索方法，通过基于深度哈希模型学习哈希码和使用 Elasticsearch 的两段式方法实现。在查询超过 120,000 个自然图像和 OpenImages 数据集中的约 6.9 百万个数据库图像的检索性能评估中，显示了 ElasticHash 具有高质量的检索结果和低查询延迟。

ElasticHash: 使用 Elasticsearch 实现深哈希进行语义图像相似度搜索

ElasticHash: Semantic Image Similarity Search by Deep Hashing with  Elasticsearch

Semantic hashing represents documents as compact binary vectors (hash codes)
and allows both efficient and effective similarity search in large-scale
information retrieval. The state of the art has primarily focused on learning
hash codes that improve similarity search effectiveness, while assuming a
brute-force linear scan strategy for searching over all the hash codes, even
though much faster alternatives exist. One such alternative is multi-index
hashing, an approach that constructs a smaller candidate set to search over,
which depending on the distribution of the hash codes can lead to sub-linear
search time. In this work, we propose Multi-Index Semantic Hashing (MISH), an
unsupervised hashing model that learns hash codes that are both effective and
highly efficient by being optimized for multi-index hashing. We derive novel
training objectives, which enable to learn hash codes that reduce the candidate
sets produced by multi-index hashing, while being end-to-end trainable. In
fact, our proposed training objectives are model agnostic, i.e., not tied to
how the hash codes are generated specifically in MISH, and are straight-forward
to include in existing and future semantic hashing models. We experimentally
compare MISH to state-of-the-art semantic hashing baselines in the task of
document similarity search. We find that even though multi-index hashing also
improves the efficiency of the baselines compared to a linear scan, they are
still upwards of 33% slower than MISH, while MISH is still able to obtain
state-of-the-art effectiveness.

本论文提出了一种基于多索引哈希的无监督哈希模型，使用全新的训练目标学习哈希码，提高多文档相似性搜索的效率，该模型相比于现有模型具有更高的效率，实验结果显示其效果优于现有语意哈希模型。