Table of Contents

Class CompositeIndexParams

Namespace
Emgu.CV.Flann
Assembly
Emgu.CV.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 : UnmanagedObject, IDisposable, IIndexParams
Inheritance
CompositeIndexParams
Implements
Inherited Members

Constructors

CompositeIndexParams(int, int, int, CenterInitType, float)

Initializes a new instance of the CompositeIndexParams class.

public CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, CenterInitType centersInit = CenterInitType.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 CenterInitType

The algorithm to use for selecting the initial centers when performing a k-means clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 )

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 chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.

Methods

DisposeObject()

Release all the memory associated with this IndexParam

protected override void DisposeObject()