#include <opencv2/flann/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 () CV_OVERRIDE

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) CV_OVERRIDE

int	getClusterCenters (Matrix< DistanceType > &centers)

IndexParams	getParameters () const CV_OVERRIDE

flann_algorithm_t	getType () const CV_OVERRIDE

void	loadIndex (FILE *stream) CV_OVERRIDE
	Loads the index from a stream.

KMeansIndex &	operator= (const KMeansIndex &)

void	saveIndex (FILE *stream) CV_OVERRIDE
	Saves the index to a stream.

void	set_cb_index (float index)

size_t	size () const CV_OVERRIDE

int	usedMemory () const CV_OVERRIDE

size_t	veclen () const CV_OVERRIDE

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.

virtual int	radiusSearch (const Matrix< ElementType > &query, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params)
	Perform radius search.

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

template<typename Distance>

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

template<typename Distance>

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

template<typename Distance>

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

Constructor & Destructor Documentation

template<typename Distance>

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

inline

Index constructor

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

template<typename Distance>

cvflann::KMeansIndex< Distance >::KMeansIndex ( const KMeansIndex< Distance > & )

template<typename Distance>

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

inlinevirtual

Index destructor.

Release the memory used by the index.

Member Function Documentation

template<typename Distance>

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

inlinevirtual

Builds the index

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

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

inline

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:

template<typename Distance>

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

inline

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:

template<typename Distance>

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

inline

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

template<typename Distance>

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

inlinevirtual

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 >.

template<typename Distance>

int cvflann::KMeansIndex< Distance >::getClusterCenters ( Matrix< DistanceType > & centers )

inline

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

template<typename Distance>

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

inlinevirtual