Name
Abstract | ||
|---|---|---|
In this paper we investigate how to scale a content based image retrieval approach beyond the RAM limits of a single computer and to make use of its hard drive to store the feature database. The feature vectors describing the images in the database are binned in multiple independent ways. Each bin contains images similar to a representative prototype. Each binning is considered through two stages of processing. First, the prototype closest to the query is found. Second, the bin corresponding to the closest prototype is fetched from disk and searched completely. The query process is repeatedly performing these two stages, each time with a binning independent of the previous ones. The scheme cuts down the hard drive access significantly and results in a major speed up. An experimental comparison between the binning scheme and a raw search shows competitive retrieval quality. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383174 | CVPR |
Keywords | Field | DocType |
hard drive based image search,binning scheme,visual databases,query process,disc drives,image retrieval,content based image retrieval,feature vectors,ram limits,feature database,competitive retrieval quality,hard discs,content-based retrieval,visualization,prototypes,information retrieval,feature extraction,feature vector | Computer vision,Feature vector,Pattern recognition,Bin,Computer science,Image retrieval,Artificial intelligence,Content based retrieval,Content-based image retrieval,Speedup | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 E-ISBN : 1-4244-1180-7 |
ISBN | Citations | PageRank |
1-4244-1180-7 | 14 | 4.49 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Friedrich Fraundorfer | 1 | 927 | 65.64 |
| Henrik Stewenius | 2 | 2777 | 165.83 |
| David Nistér | 3 | 2265 | 118.02 |