Sciweavers

Share
ICIP
2007
IEEE

Adaptive Cluster-Distance Bounding for Nearest Neighbor Search in Image Databases

11 years 11 months ago
Adaptive Cluster-Distance Bounding for Nearest Neighbor Search in Image Databases
We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index in selectively retrieving clusters that contain data entries closest to the query. Experiments conducted on real data-sets confirm the efficiency of our approach with random disk IOs reduced by 100X, as compared with the popular Vector ApproximationFile (VA-File) approach, when allowed (roughly) the same number of sequential disk accesses, with relatively low preprocessing storage and computational costs.
Sharadh Ramaswamy, Kenneth Rose
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICIP
Authors Sharadh Ramaswamy, Kenneth Rose
Comments (0)
books