Name
Abstract | ||
|---|---|---|
To improve the effectiveness of feature representation and the efficiency of feature matching, we propose a new feature representation, named Nested-SIFT, which utilizes the nesting relationship between SIFT features to group local features. A Nested-SIFT group consists of a bounding feature and several member features covered by the bounding feature. To obtain a compact representation, SimHash strategy is used to compress member features in a Nested-SIFT group into a binary code, and the similarity between two Nested-SIFT groups is efficiently computed by using the binary codes. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed Nested-SIFT approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MMUL.2013.18 | IEEE Multimedia |
Keywords | Field | DocType |
new feature representation,proposed nested-sift approach,feature representation,compress member feature,image representation,image coding,compact representation,scale invariant feature transform,image matching,nested-sift,binary code,multimedia,multimedia applications,simhash strategy,data compression,feature representation effectiveness,nested-sift group,efficient image matching,feature extraction,image retrieval,member feature compression,feature matching efficiency,transforms,binary codes,simhash,feature matching,local feature,media,information retrieval,feature recognition | Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Feature (computer vision),Feature recognition,Binary code,Image retrieval,Feature extraction,Artificial intelligence,Data compression,Bounding overwatch | Journal |
Volume | Issue | ISSN |
20 | 3 | 1070-986X |
Citations | PageRank | References |
4 | 0.44 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengfei Xu | 1 | 175 | 20.97 |
| Lei Zhang | 2 | 1754 | 89.83 |
| Kuiyuan Yang | 3 | 435 | 24.67 |
| Hongxun Yao | 4 | 2485 | 156.65 |