Name
Abstract | ||
|---|---|---|
Hashing for large scale image retrieval has become more and more popular because of its improvement in computational speed and storage reduction. Spectral Hashing (SH) is a very efficient unsupervised hashing method through mapping similar images to similar binary codes. However, it doesn't take the non-neighbor points into consideration, and its assumption of uniform data distribution is usually not true. In this paper, we propose a local linear spectral hashing framework that minimizes the average Hamming distance with a new local neighbor matrix, which can guarantee the mapping not only from neighbor images to neighbor codes, but also from non-neighbor images to non-neighbor codes. Based on the framework, we utilize three linear methods to handle the proposed problem, including orthogonal hashing, non-orthogonal hashing, and sequential hashing. The experiments on two huge datasets demonstrate the efficiency and accuracy of our methods. © Springer-Verlag 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-42051-1_36 | ICONIP (3) |
Keywords | Field | DocType |
eigenvalue decomposition,hamming distance,image retrieval,spectral hashing | Linear methods,Pattern recognition,Computer science,Matrix (mathematics),Binary code,Image retrieval,Hamming distance,Eigendecomposition of a matrix,Artificial intelligence,Hash function | Conference |
Volume | Issue | ISSN |
8228 LNCS | PART 3 | 16113349 |
Citations | PageRank | References |
1 | 0.37 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kang Zhao | 1 | 20 | 5.11 |
| Dengxiang Liu | 2 | 1 | 0.37 |
| Hongtao Lu | 3 | 735 | 93.14 |