Class AutotunedIndexParams

Namespace: Emgu.CV.Flann

Assembly: Emgu.CV.dll

When passing an object of this type the index created is automatically tuned to offer the best performance, by choosing the optimal index type (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided.

public class AutotunedIndexParams : UnmanagedObject, IDisposable, IIndexParams

Inheritance: object

DisposableObject

UnmanagedObject

AutotunedIndexParams

Implements: IDisposable

IIndexParams

Inherited Members: UnmanagedObject._ptr

UnmanagedObject.Ptr

DisposableObject.Dispose()

DisposableObject.ReleaseManagedResources()

object.GetType()

object.MemberwiseClone()

object.ToString()

object.Equals(object)

object.Equals(object, object)

object.ReferenceEquals(object, object)

object.GetHashCode()

Constructors

AutotunedIndexParams(float, float, float, float)

Initializes a new instance of the AutotunedIndexParams class.

public AutotunedIndexParams(float targetPrecision = 0.8, float buildWeight = 0.01, float memoryWeight = 0, float sampleFraction = 0.1)

Parameters

targetPrecision float: 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.
buildWeight float: Specifies the importance of the index build time reported 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.
memoryWeight float: Is used to specify the trade off 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.
sampleFraction float: 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.

Methods

DisposeObject()

Release all the memory associated with this IndexParam