cv::flann::GenericIndex< Distance > Class Template Reference

The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built. More...

#include "flann.hpp"

Public Types
typedef Distance::ResultType	DistanceType
typedef Distance::ElementType	ElementType

Public Member Functions
	GenericIndex (const Mat &features, const ::cvflann::IndexParams &params, Distance distance=Distance())
	Constructs a nearest neighbor search index for a given dataset. More...
	~GenericIndex ()
const ::cvflann::IndexParams *	getIndexParameters ()
::cvflann::IndexParams	getParameters ()
void	knnSearch (const std::vector< ElementType > &query, std::vector< int > &indices, std::vector< DistanceType > &dists, int knn, const ::cvflann::SearchParams &params)
	Performs a K-nearest neighbor search for a given query point using the index. More...
void	knnSearch (const Mat &queries, Mat &indices, Mat &dists, int knn, const ::cvflann::SearchParams &params)
int	radiusSearch (const std::vector< ElementType > &query, std::vector< int > &indices, std::vector< DistanceType > &dists, DistanceType radius, const ::cvflann::SearchParams &params)
int	radiusSearch (const Mat &query, Mat &indices, Mat &dists, DistanceType radius, const ::cvflann::SearchParams &params)
void	save (String filename)
int	size () const
int	veclen () const

Detailed Description

template<typename Distance>
class cv::flann::GenericIndex< Distance >

The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.

Member Typedef Documentation

DistanceType

template<typename Distance >

typedef Distance::ResultType cv::flann::GenericIndex< Distance >::DistanceType

ElementType

template<typename Distance >

typedef Distance::ElementType cv::flann::GenericIndex< Distance >::ElementType

Constructor & Destructor Documentation

GenericIndex()

template<typename Distance >

cv::flann::GenericIndex< Distance >::GenericIndex	(	const Mat &	features,
		const ::cvflann::IndexParams &	params,
		Distance	distance = Distance()
	)

Constructs a nearest neighbor search index for a given dataset.

Parameters

features	Matrix of containing the features(points) to index. The size of the matrix is num_features x feature_dimensionality and the data type of the elements in the matrix must coincide with the type of the index.
params	Structure containing the index parameters. The type of index that will be constructed depends on the type of this parameter. See the description.
distance	The method constructs a fast search structure from a set of features using the specified algorithm with specified parameters, as defined by params. params is a reference to one of the following class IndexParams descendants:

• LinearIndexParams When passing an object of this type, the index will perform a linear, brute-force search. :

  struct LinearIndexParams : public IndexParams
  {
  };

• KDTreeIndexParams When passing an object of this type the index constructed will consist of a set of randomized kd-trees which will be searched in parallel. :

  struct KDTreeIndexParams : public IndexParams
  {
      KDTreeIndexParams( int trees = 4 );
  };

• KMeansIndexParams When passing an object of this type the index constructed will be a hierarchical k-means tree. :

  struct KMeansIndexParams : public IndexParams
  {
      KMeansIndexParams(
          int branching = 32,
          int iterations = 11,
          flann_centers_init_t centers_init = CENTERS_RANDOM,
          float cb_index = 0.2 );
  };

• CompositeIndexParams When using a parameters object of this type the index created combines the randomized kd-trees and the hierarchical k-means tree. :

  struct CompositeIndexParams : public IndexParams
  {
      CompositeIndexParams(
          int trees = 4,
          int branching = 32,
          int iterations = 11,
          flann_centers_init_t centers_init = CENTERS_RANDOM,
          float cb_index = 0.2 );
  };

• LshIndexParams When using a parameters object of this type the index created uses multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) :

  struct LshIndexParams : public IndexParams
  {
      LshIndexParams(
          unsigned int table_number,
          unsigned int key_size,
          unsigned int multi_probe_level );
  };

• AutotunedIndexParams When passing an object of this type the index created is automatically tuned to offer the best performance, by choosing the optimal index type (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :

  struct AutotunedIndexParams : public IndexParams
  {
      AutotunedIndexParams(
          float target_precision = 0.9,
          float build_weight = 0.01,
          float memory_weight = 0,
          float sample_fraction = 0.1 );
  };

• SavedIndexParams This object type is used for loading a previously saved index from the disk. :

  struct SavedIndexParams : public IndexParams
  {
      SavedIndexParams( String filename );
  };

~GenericIndex()

template<typename Distance >

cv::flann::GenericIndex< Distance >::~GenericIndex

(

)

Member Function Documentation

getIndexParameters()

template<typename Distance >

const ::cvflann::IndexParams* cv::flann::GenericIndex< Distance >::getIndexParameters ( )

inline

getParameters()

template<typename Distance >

::cvflann::IndexParams cv::flann::GenericIndex< Distance >::getParameters ( )

inline

knnSearch() [1/2]

template<typename Distance >

void cv::flann::GenericIndex< Distance >::knnSearch	(	const std::vector< ElementType > &	query,
		std::vector< int > &	indices,
		std::vector< DistanceType > &	dists,
		int	knn,
		const ::cvflann::SearchParams &	params
	)

Performs a K-nearest neighbor search for a given query point using the index.

Parameters

query	The query point
indices	Vector that will contain the indices of the K-nearest neighbors found. It must have at least knn size.
dists	Vector that will contain the distances to the K-nearest neighbors found. It must have at least knn size.
knn	Number of nearest neighbors to search for.
params	SearchParams

knnSearch() [2/2]

template<typename Distance >

void cv::flann::GenericIndex< Distance >::knnSearch	(	const Mat &	queries,
		Mat &	indices,
		Mat &	dists,
		int	knn,
		const ::cvflann::SearchParams &	params
	)

radiusSearch() [1/2]

template<typename Distance >

int cv::flann::GenericIndex< Distance >::radiusSearch	(	const std::vector< ElementType > &	query,
		std::vector< int > &	indices,
		std::vector< DistanceType > &	dists,
		DistanceType	radius,
		const ::cvflann::SearchParams &	params
	)

radiusSearch() [2/2]

template<typename Distance >

int cv::flann::GenericIndex< Distance >::radiusSearch	(	const Mat &	query,
		Mat &	indices,
		Mat &	dists,
		DistanceType	radius,
		const ::cvflann::SearchParams &	params
	)

save()

template<typename Distance >

void cv::flann::GenericIndex< Distance >::save ( String  filename )

inline

size()

template<typename Distance >

int cv::flann::GenericIndex< Distance >::size ( ) const

inline

veclen()

template<typename Distance >

int cv::flann::GenericIndex< Distance >::veclen ( ) const

inline

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

• flann/include/opencv2/flann.hpp