cv::BOWImgDescriptorExtractor Class Reference

Class to compute an image descriptor using the bag of visual words. More...

#include <opencv2/features2d.hpp>

Public Member Functions
	BOWImgDescriptorExtractor (const Ptr< DescriptorExtractor > &dextractor, const Ptr< DescriptorMatcher > &dmatcher)
	The constructor.
	BOWImgDescriptorExtractor (const Ptr< DescriptorMatcher > &dmatcher)
virtual	~BOWImgDescriptorExtractor ()
void	compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray imgDescriptor, std::vector< std::vector< int > > *pointIdxsOfClusters=0, Mat *descriptors=0)
	Computes an image descriptor using the set visual vocabulary.
void	compute (InputArray keypointDescriptors, OutputArray imgDescriptor, std::vector< std::vector< int > > *pointIdxsOfClusters=0)
void	compute2 (const Mat &image, std::vector< KeyPoint > &keypoints, Mat &imgDescriptor)
int	descriptorSize () const
	Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
int	descriptorType () const
	Returns an image descriptor type.
const Mat &	getVocabulary () const
	Returns the set vocabulary.
void	setVocabulary (const Mat &vocabulary)
	Sets a visual vocabulary.

Protected Attributes
Ptr< DescriptorExtractor >	dextractor
Ptr< DescriptorMatcher >	dmatcher
Mat	vocabulary

Detailed Description

Class to compute an image descriptor using the bag of visual words.

Such a computation consists of the following steps:

1. Compute descriptors for a given image and its keypoints set.
2. Find the nearest visual words from the vocabulary for each keypoint descriptor.
3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the vocabulary in the given image.

Constructor & Destructor Documentation

cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor	(	const Ptr< DescriptorExtractor > &	dextractor,
		const Ptr< DescriptorMatcher > &	dmatcher
	)

Python:
	<BOWImgDescriptorExtractor object>	=	cv.BOWImgDescriptorExtractor(	dextractor, dmatcher	)

The constructor.

Parameters

dextractor	Descriptor extractor that is used to compute descriptors for an input image and its keypoints.
dmatcher	Descriptor matcher that is used to find the nearest word of the trained vocabulary for each keypoint descriptor of the image.

cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor

(

const Ptr< DescriptorMatcher > &

dmatcher

)

Python:
	<BOWImgDescriptorExtractor object>	=	cv.BOWImgDescriptorExtractor(	dextractor, dmatcher	)

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

virtual cv::BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor ( )

virtual

Member Function Documentation

void cv::BOWImgDescriptorExtractor::compute	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		OutputArray	imgDescriptor,
		std::vector< std::vector< int > > *	pointIdxsOfClusters = 0,
		Mat *	descriptors = 0
	)

Computes an image descriptor using the set visual vocabulary.

Parameters

image	Image, for which the descriptor is computed.
keypoints	Keypoints detected in the input image.
imgDescriptor	Computed output image descriptor.
pointIdxsOfClusters	Indices of keypoints that belong to the cluster. This means that pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) returned if it is non-zero.
descriptors	Descriptors of the image keypoints that are returned if they are non-zero.

void cv::BOWImgDescriptorExtractor::compute	(	InputArray	keypointDescriptors,
		OutputArray	imgDescriptor,
		std::vector< std::vector< int > > *	pointIdxsOfClusters = 0
	)

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

Parameters

keypointDescriptors	Computed descriptors to match with vocabulary.
imgDescriptor	Computed output image descriptor.
pointIdxsOfClusters	Indices of keypoints that belong to the cluster. This means that pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) returned if it is non-zero.

void cv::BOWImgDescriptorExtractor::compute2 ( const Mat &  image, std::vector< KeyPoint > &  keypoints, Mat &  imgDescriptor  )

inline

Python:
	imgDescriptor	=	cv.BOWImgDescriptorExtractor.compute(	image, keypoints[, imgDescriptor]	)

int cv::BOWImgDescriptorExtractor::descriptorSize

(

)

const

Python:
	retval	=	cv.BOWImgDescriptorExtractor.descriptorSize(		)

Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.

int cv::BOWImgDescriptorExtractor::descriptorType

(

)

const

Python:
	retval	=	cv.BOWImgDescriptorExtractor.descriptorType(		)

Returns an image descriptor type.

const Mat& cv::BOWImgDescriptorExtractor::getVocabulary

(

)

const

Python:
	retval	=	cv.BOWImgDescriptorExtractor.getVocabulary(		)

Returns the set vocabulary.

void cv::BOWImgDescriptorExtractor::setVocabulary

(

const Mat &

vocabulary

)

Python:
	None	=	cv.BOWImgDescriptorExtractor.setVocabulary(	vocabulary	)

Sets a visual vocabulary.

Parameters

vocabulary

Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the vocabulary is a visual word (cluster center).

Member Data Documentation

Ptr<DescriptorExtractor> cv::BOWImgDescriptorExtractor::dextractor

protected

Ptr<DescriptorMatcher> cv::BOWImgDescriptorExtractor::dmatcher

protected

Mat cv::BOWImgDescriptorExtractor::vocabulary

protected

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

• features2d.hpp