@Reference(type=Article, author={"Jegou, Herve","Douze, Matthijs","Schmid, Cordelia"}, title="Product Quantization for Nearest Neighbor Search", year="2011", journal="IEEE Trans. Pattern Anal. Mach. Intell.", pages={"117","","128"}, url="http://dx.doi.org/10.1109/TPAMI.2010.57", month="January", number="1", publisher="IEEE Computer Society", volume="33", customData={"issn","0162-8828","numpages","12","doi","10.1109/TPAMI.2010.57","acmid","1916695","address","Washington, DC, USA","keywords","High-dimensional indexing, High-dimensional indexing, image indexing, very large databases, approximate search., approximate search., image indexing, very large databases"}) public class FloatSDCNearestNeighbours extends FloatADCNearestNeighbours
SDC has the same computational cost as ADC, but a higher error in the computed distance, so its use is not recommended. This implementation is provided for completeness only.
data, ndims, pq
Constructor and Description |
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FloatSDCNearestNeighbours(FloatProductQuantiser pq,
float[][][] pqCentroids,
float[][] dataPoints)
Construct the SDC with the given quantiser, centroids (corresponding to
the quantiser's internal assigners), and data.
|
Modifier and Type | Method and Description |
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protected void |
computeDistances(float[] fullQuery,
BoundedPriorityQueue<IntFloatPair> queue,
IntFloatPair workingPair) |
numDimensions, searchKNN, searchKNN, searchKNN, searchNN, searchNN, searchNN, size
distanceFunc, distanceFunc, distanceFunc, distanceFunc
public FloatSDCNearestNeighbours(FloatProductQuantiser pq, float[][][] pqCentroids, float[][] dataPoints)
pq
- the Product QuantiserpqCentroids
- the centroids corresponding to the the Product Quantiser's
internal assigners.dataPoints
- the data to indexprotected void computeDistances(float[] fullQuery, BoundedPriorityQueue<IntFloatPair> queue, IntFloatPair workingPair)
computeDistances
in class FloatADCNearestNeighbours