cvflann::KMeansIndex< Distance > Class Template Reference

#include "kmeans_index.h"

Inheritance diagram for cvflann::KMeansIndex< Distance >:

Classes
class	KMeansDistanceComputer

Public Types
typedef void(KMeansIndex::*	centersAlgFunction) (int, int *, int, int *, int &)
typedef Distance::ResultType	DistanceType
typedef Distance::ElementType	ElementType

Public Member Functions
	KMeansIndex (const Matrix< ElementType > &inputData, const IndexParams &params=KMeansIndexParams(), Distance d=Distance())
	KMeansIndex (const KMeansIndex &)
virtual	~KMeansIndex ()
void	buildIndex ()
void	chooseCentersGonzales (int k, int *indices, int indices_length, int *centers, int &centers_length)
void	chooseCentersKMeanspp (int k, int *indices, int indices_length, int *centers, int &centers_length)
void	chooseCentersRandom (int k, int *indices, int indices_length, int *centers, int &centers_length)
void	findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams)
int	getClusterCenters (Matrix< DistanceType > &centers)
IndexParams	getParameters () const
flann_algorithm_t	getType () const
void	loadIndex (FILE *stream)
	Loads the index from a stream. More...
KMeansIndex &	operator= (const KMeansIndex &)
void	saveIndex (FILE *stream)
	Saves the index to a stream. More...
void	set_cb_index (float index)
size_t	size () const
int	usedMemory () const
size_t	veclen () const
Public Member Functions inherited from cvflann::NNIndex< Distance >
virtual	~NNIndex ()
virtual void	knnSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, int knn, const SearchParams &params)
	Perform k-nearest neighbor search. More...
virtual int	radiusSearch (const Matrix< ElementType > &query, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params)
	Perform radius search. More...

Public Attributes
centersAlgFunction	chooseCenters

Detailed Description

template<typename Distance>
class cvflann::KMeansIndex< Distance >

Hierarchical kmeans index

Contains a tree constructed through a hierarchical kmeans clustering and other information for indexing a set of points for nearest-neighbour matching.

Member Typedef Documentation

centersAlgFunction

template<typename Distance>

typedef void(KMeansIndex::* cvflann::KMeansIndex< Distance >::centersAlgFunction) (int, int *, int, int *, int &)

DistanceType

template<typename Distance>

typedef Distance::ResultType cvflann::KMeansIndex< Distance >::DistanceType

ElementType

template<typename Distance>

typedef Distance::ElementType cvflann::KMeansIndex< Distance >::ElementType

Constructor & Destructor Documentation

KMeansIndex() [1/2]

template<typename Distance>

cvflann::KMeansIndex< Distance >::KMeansIndex	(	const Matrix< ElementType > &	inputData,
		const IndexParams &	params = KMeansIndexParams(),
		Distance	d = Distance()
	)

Index constructor

Params: inputData = dataset with the input features params = parameters passed to the hierarchical k-means algorithm

KMeansIndex() [2/2]

template<typename Distance>

cvflann::KMeansIndex< Distance >::KMeansIndex

(

const KMeansIndex< Distance > &

)

~KMeansIndex()

template<typename Distance>

virtual cvflann::KMeansIndex< Distance >::~KMeansIndex ( )

virtual

Index destructor.

Release the memory used by the index.

Member Function Documentation

buildIndex()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::buildIndex ( )

virtual

Builds the index

Implements cvflann::NNIndex< Distance >.

chooseCentersGonzales()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::chooseCentersGonzales	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means using Gonzales' algorithm so that the centers are spaced apart from each other.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset Returns:

chooseCentersKMeanspp()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::chooseCentersKMeanspp	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means using the algorithm proposed in the KMeans++ paper: Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding

Implementation of this function was converted from the one provided in Arthur's code.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset Returns:

chooseCentersRandom()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::chooseCentersRandom	(	int	k,
		int *	indices,
		int	indices_length,
		int *	centers,
		int &	centers_length
	)

Chooses the initial centers in the k-means clustering in a random manner.

Params: k = number of centers vecs = the dataset of points indices = indices in the dataset indices_length = length of indices vector

findNeighbors()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::findNeighbors ( ResultSet< DistanceType > &  result, const ElementType *  vec, const SearchParams &  searchParams  )

virtual

Find set of nearest neighbors to vec. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors searchParams = parameters that influence the search algorithm (checks, cb_index)

Implements cvflann::NNIndex< Distance >.

getClusterCenters()

template<typename Distance>

int cvflann::KMeansIndex< Distance >::getClusterCenters

(

Matrix< DistanceType > &

centers

)

Clustering function that takes a cut in the hierarchical k-means tree and return the clusters centers of that clustering. Params: numClusters = number of clusters to have in the clustering computed Returns: number of cluster centers

getParameters()

template<typename Distance>

IndexParams cvflann::KMeansIndex< Distance >::getParameters ( ) const

virtual

Returns

The index parameters

Implements cvflann::NNIndex< Distance >.

getType()

template<typename Distance>

flann_algorithm_t cvflann::KMeansIndex< Distance >::getType ( ) const

virtual

Returns

The index type (kdtree, kmeans,...)

Implements cvflann::NNIndex< Distance >.

loadIndex()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::loadIndex ( FILE *  stream )

virtual

Loads the index from a stream.

Parameters

stream

The stream from which the index is loaded

Implements cvflann::NNIndex< Distance >.

operator=()

template<typename Distance>

KMeansIndex& cvflann::KMeansIndex< Distance >::operator=

(

const KMeansIndex< Distance > &

)

saveIndex()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::saveIndex ( FILE *  stream )

virtual

Saves the index to a stream.

Parameters

stream

The stream to save the index to

Implements cvflann::NNIndex< Distance >.

set_cb_index()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::set_cb_index

(

float

index

)

size()

template<typename Distance>

size_t cvflann::KMeansIndex< Distance >::size ( ) const

virtual

Returns size of index.

Implements cvflann::NNIndex< Distance >.

usedMemory()

template<typename Distance>

int cvflann::KMeansIndex< Distance >::usedMemory ( ) const

virtual

Computes the inde memory usage Returns: memory used by the index

Implements cvflann::NNIndex< Distance >.

veclen()

template<typename Distance>

size_t cvflann::KMeansIndex< Distance >::veclen ( ) const

virtual

Returns the length of an index feature.

Implements cvflann::NNIndex< Distance >.

Member Data Documentation

chooseCenters

template<typename Distance>

centersAlgFunction cvflann::KMeansIndex< Distance >::chooseCenters

The function used for choosing the cluster centers.

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The documentation for this class was generated from the following file:

• flann/include/opencv2/flann/kmeans_index.h