Name
Abstract | ||
|---|---|---|
By using a hypersphere to group the spatially coherent data points into the same bit, spherical hashing (SPH) can achieve a good performance in approximate nearest neighbor (ANN) search. However, when the data dimensionality rises, the data becomes sparse and the hypersphere needs to increase its radius to maintain the same coverage, which makes the data points in the hypersphere less coherent. To alleviate the effect brought from the high dimensionality of the data, a new hypersphere-based hashing method is proposed. By constructing a low-dimensional isotropic space where the variance of projection along each component is equal, both the similarity and the distribution of the original data can be preserved in this space. And then, the hashing functions are learnt by SPH in this space. The experiments on SIFT1M and GIST1M datasets show that the performance of SPH can be improved by our method and is superior to other state-of-the-art hashing methods in terms of recall and mAP performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/VCIP.2017.8305093 | 2017 IEEE Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
hashing,hypersphere,low-dimensional,isotropic,approximate nearest neighbor search | Data point,k-nearest neighbors algorithm,Isotropy,Computer science,Algorithm,Hypersphere,Curse of dimensionality,Theoretical computer science,Hash function | Conference |
ISBN | Citations | PageRank |
978-1-5386-0463-2 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinhe Lan | 1 | 0 | 0.68 |
| Zhenyu Weng | 2 | 0 | 0.34 |
| Zhu Yuesheng | 3 | 112 | 39.21 |