| OpenCV 3.4.1 Open Source Computer Vision

Classes | Public Types | Public Member Functions | Public Attributes | List of all members
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()`
| ) |  |
| 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 | ( |  | ) |
| inline virtual

Index destructor.

Release the memory used by the index.

## Member Function Documentation

## § buildIndex()

template<typename Distance>

void cvflann::KMeansIndex< Distance >::buildIndex | ( |  | ) |
| inline 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
| ) |  |
| 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
| ) |  |
| inline 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 | ) |
| 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
| inline virtual

Returns
  The index parameters

Implements cvflann::NNIndex< Distance >.

## § getType()

template<typename Distance>

flann_algorithm_t cvflann::KMeansIndex< Distance >::getType | ( |  | ) | const
| inline virtual

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

Implements cvflann::NNIndex< Distance >.

## § loadIndex()

template<typename Distance>

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

## § size()

template<typename Distance>

size_t cvflann::KMeansIndex< Distance >::size | ( |  | ) | const
| inline virtual

Returns size of index.

Implements cvflann::NNIndex< Distance >.

## § usedMemory()

template<typename Distance>

int cvflann::KMeansIndex< Distance >::usedMemory | ( |  | ) | const
| inline 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
| inline 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.

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

- flann/include/opencv2/flann/kmeans_index.h