Name
Abstract | ||
|---|---|---|
A new method is introduced that makes use of sparse image representations to search for approximate nearest neighbors (ANN) under the normalized inner-product distance. The approach relies on the construction of a new sparse vector designed to approximate the normalized inner-product between underlying signal vectors. The resulting ANN search algorithm shows significant improvement compared to querying with the original sparse vectors. The system makes use of a proposed transform that succeeds in uniformly distributing the input dataset on the unit sphere while preserving relative angular distances. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5496145 | ICASSP |
Keywords | Field | DocType |
affine transforms,image representation,search problems,sparse matrices,nearest neighbors approximation,search algorithm,sparse image representation,Sparse representations,data conditioning,indexing | Search algorithm,Pattern recognition,Computer science,Sparse approximation,Search engine indexing,Sparse image,Artificial intelligence,Artificial neural network,Sparse matrix,Computational complexity theory,Unit sphere | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.35 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquin Zepeda | 1 | 54 | 4.66 |
| Ewa Kijak | 2 | 152 | 18.31 |
| Christine Guillemot | 3 | 1286 | 104.25 |