Name
Abstract | ||
|---|---|---|
The main issues for Web-scale image retrieval are achieving good accuracy while retaining low computational time and memory footprint. This article proposes a compact image signature by aggregating tensors of visual descriptors. Efficient aggregation is achieved by preprocessing the descriptors. Compactness is achieved by projection and quantization of the signatures. The authors compare the proposed method to other efficient signatures on a 1 million images dataset and show the soundness of the approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MMUL.2013.14 | MultiMedia, IEEE |
Keywords | Field | DocType |
low computational time,compact image signature,visual descriptors,memory footprint,main issue,web-scale image retrieval,efficient signature,aggregating tensors,good accuracy,efficient aggregation,compact tensor aggregation,internet,multimedia,tensors,information retrieval,image retrieval,image processing,computer vision | Computer vision,Tensor,Computer science,Image retrieval,Image processing,Preprocessor,Artificial intelligence,Soundness,Quantization (signal processing),Memory footprint,Multimedia,Visual Word | Journal |
Volume | Issue | ISSN |
20 | 3 | 1070-986X |
Citations | PageRank | References |
18 | 0.69 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Romain Negrel | 1 | 33 | 3.42 |
| David Picard | 2 | 304 | 25.12 |
| Philippe Gosselin | 3 | 466 | 28.34 |