Pyramid encoding for fast additive quantization

Anton Muravev, Ezgi Can Ozan, Alexandros Iosifidis, Moncef Gabbouj

Abstract: The problem of approximate nearest neighbor (ANN) search in Big Data has been tackled with a variety of recent methods. Vector quantization based solutions have been maintaining the dominant position, as they operate in the original data space, better preserving inter-point distances. Additive quantization (AQ) in particular has pushed the state-of-the-art in search accuracy, but high computational costs of encoding discourage the practical application of the method. This paper proposes pyramid encoding, a novel technique, which can replace the original beam search to provide a significant complexity reduction at the cost of a slight decrease in retrieval performance. AQ with pyramid encoding is experimentally shown to obtain results comparable with the baseline method in accuracy, while offering significant computational benefits.