| Autor: |
Korfhage, Nikolaus, Mühling, Markus, Freisleben, Bernd |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
The 19th International Conference on Computer Analysis of Images and Patterns (CAIP), 2021. Lecture Notes in Computer Science, vol 13053. Springer, Cham |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-030-89131-2_2 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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