Name
Abstract | ||
|---|---|---|
Large scale approximate k-nearest neighbors search is an important and very useful technique for many multimedia retrieval applications. Most of existing search algorithms used the centralized indexing approaches and thus cannot meet the needs to search upon large scale datasets. This paper proposes an efficient and distributed approximate k-nearest neighbors search algorithm over a billion high-dimensional visual descriptors. We propose a randomized partitioning strategy and then design a two-layer distributed indexing scheme based on a neighborhood graph for large scale k-nearest neighbors search. The experimental results show that our method achieves excellent performance and scalability. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CBD.2013.20 | CBD |
Keywords | Field | DocType |
approximate k-nearest neighbors, neighborhood graph, large scale search, distributed indexing | Data mining,Graph,Algorithm design,Search algorithm,Computer science,Upper and lower bounds,Best bin first,Search engine indexing,Nearest neighbor search,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4799-3260-3 | 2 | 0.40 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhui Zhou | 1 | 2 | 0.40 |
| Chunfeng Yuan | 2 | 418 | 30.84 |
| Rong Gu | 3 | 110 | 17.77 |
| Huang, Yihua | 4 | 167 | 22.07 |