cv::BOWKMeansTrainer Class Reference

kmeans -based class to train visual vocabulary using the bag of visual words approach. : More...

#include "features2d.hpp"

Inheritance diagram for cv::BOWKMeansTrainer:

Public Member Functions
	BOWKMeansTrainer (int clusterCount, const TermCriteria &termcrit=TermCriteria(), int attempts=3, int flags=KMEANS_PP_CENTERS)
	The constructor. More...
virtual	~BOWKMeansTrainer ()
virtual Mat	cluster () const
virtual Mat	cluster (const Mat &descriptors) const
	Clusters train descriptors. More...
Public Member Functions inherited from cv::BOWTrainer
	BOWTrainer ()
virtual	~BOWTrainer ()
void	add (const Mat &descriptors)
	Adds descriptors to a training set. More...
virtual void	clear ()
int	descriptorsCount () const
	Returns the count of all descriptors stored in the training set. More...
const std::vector< Mat > &	getDescriptors () const
	Returns a training set of descriptors. More...

Protected Attributes
int	attempts
int	clusterCount
int	flags
TermCriteria	termcrit
Protected Attributes inherited from cv::BOWTrainer
std::vector< Mat >	descriptors
int	size

Detailed Description

kmeans -based class to train visual vocabulary using the bag of visual words approach. :

Constructor & Destructor Documentation

cv::BOWKMeansTrainer::BOWKMeansTrainer	(	int	clusterCount,
		const TermCriteria &	termcrit = TermCriteria(),
		int	attempts = 3,
		int	flags = KMEANS_PP_CENTERS
	)

The constructor.

See also

cv::kmeans

virtual cv::BOWKMeansTrainer::~BOWKMeansTrainer ( )

virtual

Member Function Documentation

virtual Mat cv::BOWKMeansTrainer::cluster ( ) const

virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Implements cv::BOWTrainer.

virtual Mat cv::BOWKMeansTrainer::cluster ( const Mat &  descriptors ) const

virtual

Clusters train descriptors.

Parameters

descriptors

Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered.

Implements cv::BOWTrainer.

Member Data Documentation

int cv::BOWKMeansTrainer::attempts

protected

int cv::BOWKMeansTrainer::clusterCount

protected

int cv::BOWKMeansTrainer::flags

protected

TermCriteria cv::BOWKMeansTrainer::termcrit

protected

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

• features2d/include/opencv2/features2d.hpp