Surrogate text representation of visual features for fast image retrieval

We propose a simple and effective methodology to index and retrieve image features without the need for a time-consuming codebook learning step. We employ a scalar quantization approach combined with Surrogate Text Representation (STR) to perform large-scale image retrieval relying on the latest text search engine technologies. Experiments on large-scale image retrieval benchmarks show that we improve the effectiveness-efficiency trade-off of current STR approaches while performing comparably to state-of-the-art main-memory methods without requiring a codebook learning procedure.