DBoW2::TemplatedVocabulary< TDescriptor, F > Class Template Reference

Generic Vocabulary. More...

#include <TemplatedVocabulary.h>

Classes
struct	Node
	Tree node. More...
Public Member Functions
	TemplatedVocabulary (int k=10, int L=5, WeightingType weighting=TF_IDF, ScoringType scoring=L1_NORM)
	TemplatedVocabulary (const std::string &filename)
	TemplatedVocabulary (const char *filename)
	TemplatedVocabulary (const TemplatedVocabulary< TDescriptor, F > &voc)
virtual	~TemplatedVocabulary ()
TemplatedVocabulary < TDescriptor, F > &	operator= (const TemplatedVocabulary< TDescriptor, F > &voc)
virtual void	create (const std::vector< std::vector< TDescriptor > > &training_features)
virtual void	create (const std::vector< std::vector< TDescriptor > > &training_features, int k, int L)
virtual void	create (const std::vector< std::vector< TDescriptor > > &training_features, int k, int L, WeightingType weighting, ScoringType scoring)
virtual unsigned int	size () const
virtual bool	empty () const
virtual void	transform (const std::vector< TDescriptor > &features, BowVector &v) const
virtual void	transform (const std::vector< TDescriptor > &features, BowVector &v, FeatureVector &fv, int levelsup) const
virtual WordId	transform (const TDescriptor &feature) const
double	score (const BowVector &a, const BowVector &b) const
virtual NodeId	getParentNode (WordId wid, int levelsup) const
void	getWordsFromNode (NodeId nid, std::vector< WordId > &words) const
int	getBranchingFactor () const
int	getDepthLevels () const
float	getEffectiveLevels () const
virtual TDescriptor	getWord (WordId wid) const
virtual WordValue	getWordWeight (WordId wid) const
WeightingType	getWeightingType () const
ScoringType	getScoringType () const
void	setWeightingType (WeightingType type)
void	setScoringType (ScoringType type)
void	save (const std::string &filename) const
void	load (const std::string &filename)
virtual void	save (cv::FileStorage &fs, const std::string &name="vocabulary") const
virtual void	load (const cv::FileStorage &fs, const std::string &name="vocabulary")
virtual int	stopWords (double minWeight)
Protected Types
typedef const TDescriptor *	pDescriptor
	Pointer to descriptor.
Protected Member Functions
void	createScoringObject ()
void	getFeatures (const vector< vector< TDescriptor > > &training_features, vector< pDescriptor > &features) const
virtual void	transform (const TDescriptor &feature, WordId &id, WordValue &weight, NodeId *nid=NULL, int levelsup=0) const
virtual void	transform (const TDescriptor &feature, WordId &id) const
void	HKmeansStep (NodeId parent_id, const vector< pDescriptor > &descriptors, int current_level)
virtual void	initiateClusters (const vector< pDescriptor > &descriptors, vector< TDescriptor > &clusters) const
void	initiateClustersKMpp (const vector< pDescriptor > &descriptors, vector< TDescriptor > &clusters) const
void	createWords ()
void	setNodeWeights (const vector< vector< TDescriptor > > &features)
Protected Attributes
int	m_k
	Branching factor.
int	m_L
	Depth levels.
WeightingType	m_weighting
	Weighting method.
ScoringType	m_scoring
	Scoring method.
GeneralScoring *	m_scoring_object
	Object for computing scores.
std::vector< Node >	m_nodes
	Tree nodes.
std::vector< Node * >	m_words

Detailed Description

template<class TDescriptor, class F>
class DBoW2::TemplatedVocabulary< TDescriptor, F >

Generic Vocabulary.

Parameters:

	TDescriptor	class of descriptor
	F	class of descriptor functions

Constructor & Destructor Documentation

template<class TDescriptor , class F >

DBoW2::TemplatedVocabulary< TDescriptor, F >::TemplatedVocabulary	(	int	k = `10`,
		int	L = `5`,
		WeightingType	weighting = `TF_IDF`,
		ScoringType	scoring = `L1_NORM`
	)			`[inline]`

Initiates an empty vocabulary

Parameters:

	k	branching factor
	L	depth levels
	weighting	weighting type
	scoring	scoring type

template<class TDescriptor , class F >

DBoW2::TemplatedVocabulary< TDescriptor, F >::TemplatedVocabulary ( const std::string & filename ) [inline]

Creates the vocabulary by loading a file

Parameters:

filename

template<class TDescriptor , class F >

DBoW2::TemplatedVocabulary< TDescriptor, F >::TemplatedVocabulary ( const char * filename ) [inline]

Creates the vocabulary by loading a file

Parameters:

filename

template<class TDescriptor , class F >

DBoW2::TemplatedVocabulary< TDescriptor, F >::TemplatedVocabulary ( const TemplatedVocabulary< TDescriptor, F > & voc ) [inline]

Copy constructor

Parameters:

voc

template<class TDescriptor , class F >

DBoW2::TemplatedVocabulary< TDescriptor, F >::~TemplatedVocabulary ( ) [inline, virtual]

Destructor

Member Function Documentation

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::create	(	const std::vector< std::vector< TDescriptor > > &	training_features,
		int	k,
		int	L,
		WeightingType	weighting,
		ScoringType	scoring
	)			`[inline, virtual]`

Creates a vocabulary from the training features, setting the branching factor nad the depth levels of the tree, and the weighting and scoring schemes

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::create	(	const std::vector< std::vector< TDescriptor > > &	training_features,
		int	k,
		int	L
	)			`[inline, virtual]`

Creates a vocabulary from the training features, setting the branching factor and the depth levels of the tree

Parameters:

	training_features
	k	branching factor
	L	depth levels

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::create ( const std::vector< std::vector< TDescriptor > > & training_features ) [inline, virtual]

Creates a vocabulary from the training features with the already defined parameters

Parameters:

training_features

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::createScoringObject ( ) [inline, protected]

Creates an instance of the scoring object accoring to m_scoring

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::createWords ( ) [inline, protected]

Create the words of the vocabulary once the tree has been built

template<class TDescriptor , class F >

bool DBoW2::TemplatedVocabulary< TDescriptor, F >::empty ( ) const [inline, virtual]

Returns whether the vocabulary is empty (i.e. it has not been trained)

Returns:: true iff the vocabulary is empty

template<class TDescriptor, class F>

int DBoW2::TemplatedVocabulary< TDescriptor, F >::getBranchingFactor ( ) const [inline]

Returns the branching factor of the tree (k)

Returns:: k

template<class TDescriptor, class F>

int DBoW2::TemplatedVocabulary< TDescriptor, F >::getDepthLevels ( ) const [inline]

Returns the depth levels of the tree (L)

Returns:: L

template<class TDescriptor , class F >

float DBoW2::TemplatedVocabulary< TDescriptor, F >::getEffectiveLevels ( ) const [inline]

Returns the real depth levels of the tree on average

Returns:: average of depth levels of leaves

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::getFeatures	(	const vector< vector< TDescriptor > > &	training_features,
		vector< pDescriptor > &	features
	)			const `[inline, protected]`

Returns a set of pointers to descriptores

Parameters:

	training_features	all the features
	features	(out) pointers to the training features

template<class TDescriptor , class F >

NodeId DBoW2::TemplatedVocabulary< TDescriptor, F >::getParentNode	(	WordId	wid,
		int	levelsup
	)			const `[inline, virtual]`

Returns the id of the node that is "levelsup" levels from the word given

Parameters:

	wid	word id
	levelsup	0..L

Returns:: node id. if levelsup is 0, returns the node id associated to the word id

template<class TDescriptor, class F>

ScoringType DBoW2::TemplatedVocabulary< TDescriptor, F >::getScoringType ( ) const [inline]

Returns the scoring method

Returns:: scoring method

template<class TDescriptor, class F>

WeightingType DBoW2::TemplatedVocabulary< TDescriptor, F >::getWeightingType ( ) const [inline]

Returns the weighting method

Returns:: weighting method

template<class TDescriptor , class F >

TDescriptor DBoW2::TemplatedVocabulary< TDescriptor, F >::getWord ( WordId wid ) const [inline, virtual]

Returns the descriptor of a word

Parameters:

wid

word id

Returns:: descriptor

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::getWordsFromNode	(	NodeId	nid,
		std::vector< WordId > &	words
	)			const `[inline]`

Returns the ids of all the words that are under the given node id, by traversing any of the branches that goes down from the node

Parameters:

	nid	starting node id
	words	ids of words

template<class TDescriptor , class F >

WordValue DBoW2::TemplatedVocabulary< TDescriptor, F >::getWordWeight ( WordId wid ) const [inline, virtual]

Returns the weight of a word

Parameters:

wid

word id

Returns:: weight

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::HKmeansStep	(	NodeId	parent_id,
		const vector< pDescriptor > &	descriptors,
		int	current_level
	)			`[inline, protected]`

Creates a level in the tree, under the parent, by running kmeans with a descriptor set, and recursively creates the subsequent levels too

Parameters:

	parent_id	id of parent node
	descriptors	descriptors to run the kmeans on
	current_level	current level in the tree

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::initiateClusters	(	const vector< pDescriptor > &	descriptors,
		vector< TDescriptor > &	clusters
	)			const `[inline, protected, virtual]`

Creates k clusters from the given descriptors with some seeding algorithm.

Note:: In this class, kmeans++ is used, but this function should be overriden by inherited classes.

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::initiateClustersKMpp	(	const vector< pDescriptor > &	descriptors,
		vector< TDescriptor > &	clusters
	)			const `[inline, protected]`

Creates k clusters from the given descriptor sets by running the initial step of kmeans++

Parameters:

	descriptors
	clusters	resulting clusters

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::load	(	const cv::FileStorage &	fs,
		const std::string &	name = `"vocabulary"`
	)			`[inline, virtual]`

Loads the vocabulary from a file storage node

Parameters:

	fn	first node
	subname	name of the child node of fn where the tree is stored. If not given, the fn node is used instead

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::load ( const std::string & filename ) [inline]

Loads the vocabulary from a file

Parameters:

filename

template<class TDescriptor , class F >

TemplatedVocabulary< TDescriptor, F > & DBoW2::TemplatedVocabulary< TDescriptor, F >::operator= ( const TemplatedVocabulary< TDescriptor, F > & voc ) [inline]

Assigns the given vocabulary to this by copying its data and removing all the data contained by this vocabulary before

Parameters:

voc

Returns:: reference to this vocabulary

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::save	(	cv::FileStorage &	fs,
		const std::string &	name = `"vocabulary"`
	)			const `[inline, virtual]`

Saves the vocabulary to a file storage structure

Parameters:

fn

node in file storage

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::save ( const std::string & filename ) const [inline]

Saves the vocabulary into a file

Parameters:

filename

template<class TDescriptor , class F >

double DBoW2::TemplatedVocabulary< TDescriptor, F >::score	(	const BowVector &	a,
		const BowVector &	b
	)			const `[inline]`

Returns the score of two vectors

Parameters:

	a	vector
	b	vector

Returns:: score between vectors

Note:: the vectors must be already sorted and normalized if necessary

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::setNodeWeights ( const vector< vector< TDescriptor > > & features ) [inline, protected]

Sets the weights of the nodes of tree according to the given features. Before calling this function, the nodes and the words must be already created (by calling HKmeansStep and createWords)

Parameters:

features

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::setScoringType ( ScoringType type ) [inline]

Changes the scoring method

Parameters:

type

new scoring type

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::setWeightingType ( WeightingType type ) [inline]

Changes the weighting method

Parameters:

type

new weighting type

template<class TDescriptor , class F >

unsigned int DBoW2::TemplatedVocabulary< TDescriptor, F >::size ( ) const [inline, virtual]

Returns the number of words in the vocabulary

Returns:: number of words

template<class TDescriptor , class F >

int DBoW2::TemplatedVocabulary< TDescriptor, F >::stopWords ( double minWeight ) [inline, virtual]

Stops those words whose weight is below minWeight. Words are stopped by setting their weight to 0. There are not returned later when transforming image features into vectors. Note that when using IDF or TF_IDF, the weight is the idf part, which is equivalent to -log(f), where f is the frequency of the word (f = Ni/N, Ni: number of training images where the word is present, N: number of training images). Note that the old weight is forgotten, and subsequent calls to this function with a lower minWeight have no effect.

Returns:: number of words stopped now

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::transform	(	const TDescriptor &	feature,
		WordId &	id
	)			const `[inline, protected, virtual]`

Returns the word id associated to a feature

Parameters:

	feature
	id	(out) word id

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::transform	(	const TDescriptor &	feature,
		WordId &	id,
		WordValue &	weight,
		NodeId *	nid = `NULL`,
		int	levelsup = `0`
	)			const `[inline, protected, virtual]`

Returns the word id associated to a feature

Parameters:

	feature
	id	(out) word id
	weight	(out) word weight
	nid	(out) if given, id of the node "levelsup" levels up
	levelsup

template<class TDescriptor , class F >

WordId DBoW2::TemplatedVocabulary< TDescriptor, F >::transform ( const TDescriptor & feature ) const [inline, virtual]

Transforms a single feature into a word (without weight)

Parameters:

feature

Returns:: word id

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::transform	(	const std::vector< TDescriptor > &	features,
		BowVector &	v,
		FeatureVector &	fv,
		int	levelsup
	)			const `[inline, virtual]`

Transform a set of descriptors into a bow vector and a feature vector

Parameters:

	features
	v	(out) bow vector
	fv	(out) feature vector of nodes and feature indexes
	levelsup	levels to go up the vocabulary tree to get the node index

template<class TDescriptor , class F >

void DBoW2::TemplatedVocabulary< TDescriptor, F >::transform	(	const std::vector< TDescriptor > &	features,
		BowVector &	v
	)			const `[inline, virtual]`

Transforms a set of descriptores into a bow vector

Parameters:

	features
	v	(out) bow vector of weighted words

Member Data Documentation

template<class TDescriptor, class F>

std::vector<Node*> DBoW2::TemplatedVocabulary< TDescriptor, F >::m_words [protected]

Words of the vocabulary (tree leaves) this condition holds: m_words[wid]->word_id == wid

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

TemplatedVocabulary.h

DBoW2::TemplatedVocabulary< TDescriptor, F > Class Template Reference

Classes

Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Detailed Description

template<class TDescriptor, class F> class DBoW2::TemplatedVocabulary< TDescriptor, F >

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<class TDescriptor, class F>
class DBoW2::TemplatedVocabulary< TDescriptor, F >