Large-scale instance-level image retrieval

Highlights：

• We propose a novel approach to tackle large-scale image retrieval on deep-learned image descriptors by transforming the vectorial descriptors into surrogate text encodings.

• This transformation is based on a scalar quantization approach that is specifically designed to generate text suitable for scalable indexing in secondary memory.

• Our approach allows us to conveniently reuse mature and scalable full-text search engine technology (e.g. Elasticsearch, Apache Lucene) for retrieving images on a large scale without the need for dedicated structures.

• We compare our proposal to other works in a unified framework for representing surrogate text representation transformations.

• We performed an extensive experimental evaluation to assess the effectiveness and efficiency of our proposal, and compare it to other state-of-the-art vector-tailored indexing approaches.