Class CompositeIndexParams

Namespace

OpenCvSharp.Flann

Assembly

OpenCvSharp.dll

When using a parameters object of this type the index created combines the randomized kd-trees and the hierarchical k-means tree.

public class CompositeIndexParams : IndexParams, IDisposable, ICvPtrHolder

Inheritance

object

DisposableObject

DisposableCvObject

IndexParams

CompositeIndexParams

Implements

IDisposable

ICvPtrHolder

Inherited Members

IndexParams.DisposeManaged()

IndexParams.GetString(string, string)

IndexParams.GetInt(string, int)

IndexParams.GetDouble(string, double)

IndexParams.SetString(string, string)

IndexParams.SetInt(string, int)

IndexParams.SetDouble(string, double)

IndexParams.SetFloat(string, float)

IndexParams.SetBool(string, bool)

IndexParams.SetAlgorithm(int)

DisposableCvObject.ptr

DisposableCvObject.DisposeUnmanaged()

DisposableCvObject.CvPtr

DisposableObject.DataHandle

DisposableObject.IsDisposed

DisposableObject.IsEnabledDispose

DisposableObject.AllocatedMemory

DisposableObject.AllocatedMemorySize

DisposableObject.Dispose()

DisposableObject.Dispose(bool)

DisposableObject.AllocGCHandle(object)

DisposableObject.AllocMemory(int)

DisposableObject.NotifyMemoryPressure(long)

DisposableObject.ThrowIfDisposed()

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

CompositeIndexParams(Ptr)

protected CompositeIndexParams(Ptr ptrObj)

Parameters

ptrObj Ptr

CompositeIndexParams(int, int, int, FlannCentersInit, float)

public CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2)

Parameters

trees int

The number of parallel kd-trees to use. Good values are in the range [1..16]

branching int

The branching factor to use for the hierarchical k-means tree

iterations int

The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence

centersInit FlannCentersInit

The algorithm to use for selecting the initial centers when performing a k-means clustering step.

cbIndex float

This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.