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cvflann::KMeansIndex< Distance > Class Template Reference

#include "kmeans_index.h"

Inheritance diagram for cvflann::KMeansIndex< Distance >:
cvflann::NNIndex< 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. More...
 
KMeansIndexoperator= (const KMeansIndex &)
 
void saveIndex (FILE *stream) CV_OVERRIDE
 Saves the index to a stream. More...
 
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. 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() 
)
inline

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 ( )
inlinevirtual

Index destructor.

Release the memory used by the index.

Member Function Documentation

§ buildIndex()

template<typename Distance>
void cvflann::KMeansIndex< Distance >::buildIndex ( )
inlinevirtual

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 
)
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:

§ chooseCentersKMeanspp()

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:

§ chooseCentersRandom()

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

§ findNeighbors()

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 >.

§ getClusterCenters()

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

§ getParameters()

template<typename Distance>
IndexParams cvflann::KMeansIndex< Distance >::getParameters ( ) const
inlinevirtual
Returns
The index parameters

Implements cvflann::NNIndex< Distance >.

§ getType()

template<typename Distance>
flann_algorithm_t cvflann::KMeansIndex< Distance >::getType ( ) const
inlinevirtual
Returns
The index type (kdtree, kmeans,...)

Implements cvflann::NNIndex< Distance >.

§ loadIndex()

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

Loads the index from a stream.

Parameters
streamThe 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)
inlinevirtual

Saves the index to a stream.

Parameters
streamThe 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)
inline

§ size()

template<typename Distance>
size_t cvflann::KMeansIndex< Distance >::size ( ) const
inlinevirtual

Returns size of index.

Implements cvflann::NNIndex< Distance >.

§ usedMemory()

template<typename Distance>
int cvflann::KMeansIndex< Distance >::usedMemory ( ) const
inlinevirtual

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
inlinevirtual

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.


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