Time-Quality Tradeoff of MuseHash Query Processing Performance

Nowadays, massive quantities of multimedia data, such as videos, images, text and audio, are generated by various applications on smartphones, drones and other devices. To facilitate efficient retrieval from these multimedia collections, we need (a) effective media representation and (b) efficient indexing and query processing approaches. Recently, the MuseHash approach was proposed, which can effectively represent a variety of modalities, improving on previous hashing-based approaches. However, the interaction of the MuseHash approach with existing indexing and query processing  approaches has not been considered. This paper provides a systematic evaluation of a set of state-of-the-art approximate nearest neighbor search algorithms for image retrieval, when applied to the MuseHash approach, providing quantitative comparison results and evaluating the use of High-Performance Computing (HPC) infrastructures.  An extensive set of experiments on a benchmark aerial dataset and on a real life-log dataset demonstrates the effectiveness of employing hashing and ANN techniques with HPC, resulting in reduced computational time.