Name
Abstract | ||
|---|---|---|
This paper introduces a group testing framework for detecting large similarities between high-dimensional vectors, such as descriptors used in state-of-the-art description of multimedia documents.At the crossroad of multimedia information retrieval and signal processing, we produce a set of group representations that jointly encode several vectors into a single one, in the spirit of group testing approaches. By comparing a query vector to several of these intermediate representations, we screen the large values taken by the similarities between the query and all the vectors, at a fraction of the cost of exhaustive similarity calculation. Unlike concurrent indexing methods that suffer from the curse of dimensionality, our method exploits the properties of high-dimensional spaces. It therefore complements other strategies for approximate nearest neighbor search. Our preliminary experiments demonstrate the potential of group testing for searching large databases of multimedia objects represented by vectors. We obtain a large improvement in terms of the theoretical complexity, at the cost of a small or negligible decrease of accuracy.We hope that this preliminary work will pave the way to subsequent works for multimedia retrieval with limited resources. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2647868.2654895 | ACM Multimedia 2001 |
Keywords | Field | DocType |
high-dimensional spaces,compressed sensing,group testing,image retrieval,similarity search,large-scale databases,retrieval models | ENCODE,Data mining,Group representation,Computer science,Multimedia information retrieval,Image retrieval,Search engine indexing,Theoretical computer science,Curse of dimensionality,Group testing,Nearest neighbor search | Conference |
Citations | PageRank | References |
23 | 0.74 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miaojing Shi | 1 | 186 | 11.27 |
| Teddy Furon | 2 | 660 | 55.04 |
| Hervé Jégou | 3 | 5682 | 247.98 |