Name
Abstract | ||
|---|---|---|
Hashing, which encodes data points into binary codes, has become a popular approach for approximate nearest neighbor searching tasks because of its storage and retrieval efficiency. Given a pool of bits, we propose to select a set of bits according to a quality measurement directly related to the large scale approximate nearest neighbor searching problem. An alternating greedy optimization method is proposed to find a locally optimal solution. The experimental results show this optimization method is efficient and effective. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IVCNZ.2015.7761538 | 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | Field | DocType |
hashing bits,data point encodes,binary codes,quality measurement,large scale approximate nearest neighbor searching problem,greedy optimization method,computational complexity | k-nearest neighbors algorithm,Data point,Locality-sensitive hashing,Computer science,Hamming(7,4),Binary code,Theoretical computer science,Hamming distance,Hash function,Nearest neighbor search | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-5090-0358-7 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiping Fu | 1 | 0 | 0.68 |
| brendan mccane | 2 | 223 | 33.05 |
| Steven Mills | 3 | 41 | 17.74 |
| Michael H. Albert | 4 | 157 | 28.36 |