在多维空间中的聚类和搜索


struct   cv::flann::CvType< T >
 
struct   cv::flann::CvType< char >
 
struct   cv::flann::CvType< double >
 
struct   cv::flann::CvType< float >
 
struct   cv::flann::CvType< short >
 
struct   cv::flann::CvType< unsigned char >
 
struct   cv::flann::CvType< unsigned short >
 
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. 更多...
 

函数

template<typename Distance >
int  cv::flann::hierarchicalClustering (const Mat &features, Mat &centers, const ::cvflann::KMeansIndexParams &params, Distance d=Distance())
  Clusters features using hierarchical k-means algorithm. 更多...
 

详细描述

This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. More information about FLANN can be found in [167] .

函数文档编制

◆  hierarchicalClustering()

template<typename Distance >
int cv::flann::hierarchicalClustering ( const Mat features ,
Mat centers ,
const ::cvflann::KMeansIndexParams &  params ,
Distance  d = Distance()  
)

#include < opencv2/flann.hpp >

Clusters features using hierarchical k-means algorithm.

Parameters
features The points to be clustered. The matrix must have elements of type Distance::ElementType.
centers The centers of the clusters obtained. The matrix must have type Distance::ResultType. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form (branching-1)*k+1 that's lower than the number of clusters desired, where branching is the tree's branching factor (see description of the KMeansIndexParams).
params Parameters used in the construction of the hierarchical k-means tree.
d Distance to be used for clustering.

The method clusters the given feature vectors by constructing a hierarchical k-means tree and choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters found.