Parallel similarity search based on the dimensions value cardinalities of image descriptor vectors

Dimitrios Rafailidis, Yannis Manolopoulos
2015 Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15  
In this paper, we propose a parallel similarity search strategy based on the dimensions value cardinalities, an inherit characteristic of image descriptor vectors. Our strategy has low preprocessing requirements by dividing the computational cost of the preprocessing steps into several machines and locating the descriptors with similar dimensions value cardinalities logically close. Additionally, an efficient parallel query processing algorithm is proposed, where the dimensions of image
more » ... ors are prioritized in the searching strategy, assuming that dimensions of high value cardinalities have more discriminative power than the dimensions of low ones. In our experiments with publicly available datasets of 80 million and 1 billion images, we show that the proposed method outperforms state-of-the-art parallel similarity search strategies, in terms of preprocessing cost, search time and accuracy. Finally, we made our source code publicly available.
doi:10.1145/2695664.2695857 dblp:conf/sac/RafailidisM15 fatcat:atbqtuvmzbdybd4z54qsxen5ka