Class AutotunedIndexParams

hierarchical k-means tree.

Inheritance
System.Object
DisposableObject
DisposableCvObject
IndexParams
AutotunedIndexParams
Implements
Inherited Members
Namespace: OpenCvSharp.Flann
Assembly: OpenCvSharp.dll
Syntax
public class AutotunedIndexParams : IndexParams, ICvPtrHolder

Constructors

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AutotunedIndexParams(Ptr)

Declaration
protected AutotunedIndexParams(Ptr ptrObj)
Parameters
Type Name Description
Ptr ptrObj
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AutotunedIndexParams(Single, Single, Single, Single)

Declaration
public AutotunedIndexParams(float targetPrecision = 0.9F, float buildWeight = 0.01F, float memoryWeight = 0F, float sampleFraction = 0.1F)
Parameters
Type Name Description
System.Single targetPrecision

Is a number between 0 and 1 specifying the percentage of the approximate nearest-neighbor searches that return the exact nearest-neighbor. Using a higher value for this parameter gives more accurate results, but the search takes longer. The optimum value usually depends on the application.
System.Single buildWeight

Specifies the importance of the index build time raported to the nearest-neighbor search time. In some applications it’s acceptable for the index build step to take a long time if the subsequent searches in the index can be performed very fast. In other applications it’s required that the index be build as fast as possible even if that leads to slightly longer search times.
System.Single memoryWeight

Is used to specify the tradeoff between time (index build time and search time) and memory used by the index. A value less than 1 gives more importance to the time spent and a value greater than 1 gives more importance to the memory usage.
System.Single sampleFraction

Is a number between 0 and 1 indicating what fraction of the dataset to use in the automatic parameter configuration algorithm. Running the algorithm on the full dataset gives the most accurate results, but for very large datasets can take longer than desired. In such case using just a fraction of the data helps speeding up this algorithm while still giving good approximations of the optimum parameters.

Implements