PUBLIC

NNetDbC::NNetDbC(void) NNetDbC::NNetDbC(istream &) NNetDbC::~NNetDbC(void) NNetDbC::Save(ostream &) NNetDbC::Train(IntT) NNetDbC::Train(IntT,const NumVLDataSetC &) NNetDbC::Train(IntT,const NumVLDataSetC &,IntT,FilenameC,const FilenameC &) NNetDbC::PixelSearch(StringC &,StringC &) NNetDbC::ClusterSearch(StringC &,StringC &,RealT) NNetDbC::ClusterSearch(StringC &,StringC &,RealT,RealT (*)(FuncArgList)) NNetDbC::Search(StringC &,StringC &,RealT) NNetDbC::Search(const DatabaseH &,RealT) NNetDbC::Net(void) NNetDbC::MakeSetFromRegions(IntT,FilenameC,const FilenameC &) ImageDbC::Copy(void) const ImageDbC::operator=(const ImageDbC &) ImageDbC::RootDir(void) const ImageDbC::ImageDir(void) const ImageDbC::LabelDir(void) const ImageDbC::ExpDir(void) const ImageDbC::FeatDir(void) const ImageDbC::FeatDir(StringC) const ImageDbC::ResultsDir(void) const ImageDbC::IndicesDir(void) const ImageDbC::CreateResultsDir(void) ImageDbC::GenerateQuery(const FilenameC &,const FilenameC &,RealT) ImageDbC::GenerateQuery(const FilenameC &,const FilenameC &,IntT,IntT) ImageDbC::GenerateQuery(const FilenameC &,const FilenameC &,IntT,RealT) ImageDbC::GenerateQuery(const DListC &,const FilenameC &,IntT,IntT) ImageDbC::CreateDataSet(FilenameC &,FilenameC &,IntT) ImageDbC::GetFeatureImages(const FilenameC &,const FilenameC &) ImageDbC::GetFeatureImages(const FilenameC &,const DListC &) ImageDbC::Save(ostream &) const ImageDbC::SaveAsHtml(ostream &,const StringC &) const ImageDbC::Data(void) ImageDbC::Data(void) const ImageDbC::SetDataSet(NumVLDataSetC &)

NNetDbC
include	"amma/visualdb/NNetDb.hh"
User Level:	Default
Library:	NNetDb
Example:	exIndex.cc
Section:	default.Kieron J Messer
In Scope:	std

Parent Classes:

public ImageDbC

NumModel2LayerNetC _net;

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Neural net model, no inputs

VecMatC _norm;

The normalisation coefficients

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IntSArray1dC _selected;

Features selected by selection algorithm

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NumModelQuadraticC _net; Quadratic model, no inputs NumModelPolynomialC _net; Polynomial model, int input The network used to make the classification decision This should in time be able to be swapped with any Numeric Model and work the same.

NNetDbC()

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Empty constructor

NNetDbC(istream & in)

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Construct from input stream

~NNetDbC()

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Virtual destructor

void Save(ostream & out)

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Save the neural network query object

void Train(IntT numOfSamples)

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Train the neural network on the query

void Train(IntT numOfSamples,const NumVLDataSetC & WorldDataSet)

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Train the neural network on the query and a world dataset

void Train(IntT numOfSamples,const NumVLDataSetC & WorldDataSet,IntT numFromRegion,FilenameC listOfRegions,const FilenameC & FeatFile)

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===================================== Train the neural network using the a specified number of samples from the passed world data set Also tran on a specified number of samples from each region passed in the list Added by Lee Gregory 16th March 2000 ======================================

void PixelSearch(StringC & ImageFile,StringC & FeatFile)

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Test the query on images specified in file

ImageSetC ClusterSearch(StringC & ImageFile,StringC & ExtraPath,RealT thres)

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Test the query on images specified in file

ImageSetC ClusterSearch(StringC & ImageFile,StringC & ExtraPath,RealT thres,RealT (*)(FuncArgList) scoring)

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RCHashARC<StringC,ImageRegSetC> Search(StringC & ImageFile,StringC & ExtraPath,RealT thre)

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Test the query on images specified in file

RCHashARC<StringC,ImageRegSetC> Search(const DatabaseH & indices,RealT thre)

perform traditional fs

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NumModel2LayerNetC Net()

Access to neural net

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NumVLDataSetC MakeSetFromRegions(IntT numFromRegion,FilenameC listOfRegions,const FilenameC & FeatFile)

Makes a data set of features from a set of sub images : note the labels are zero

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========================= Object Representation // =========================

ImageDbC & Copy() const

Make a copy

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ImageDbC & operator=(const ImageDbC & db)

Assignment of big object

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const FilenameC RootDir() const

Return the root of the database structure

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const FilenameC ImageDir() const

Return the image directory

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const FilenameC LabelDir() const

Return the label directory

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const FilenameC ExpDir() const

Return the experimental directory

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const FilenameC FeatDir() const

Return the feature directory

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const FilenameC FeatDir(StringC image) const

Return the feature directory for a given image

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const FilenameC ResultsDir() const

Return the directory where the results are to go

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const FilenameC IndicesDir() const

Return the directory where the indices reside

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void CreateResultsDir()

Create the query dataset from the user input files

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void GenerateQuery(const FilenameC & QueryFile,const FilenameC & FeatFile,RealT scale = 1.0)

Generate the query from the user specified files

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void GenerateQuery(const FilenameC & QueryFile,const FilenameC & FeatFile,IntT border,IntT label = 1)

Generate the query from the user specified files

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void GenerateQuery(const FilenameC & QueryFile,const FilenameC & FeatFile,IntT border,RealT scale)

Generate the query from the user specified files

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void GenerateQuery(const DListC<StringC> & ImageFileList,const FilenameC & FeatFile,IntT border,IntT label = 1)

Generate the query from a list of user specified files The query is made by taking the union of all image regions in the list Added by Lee Gregory - 15th March 00

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void CreateDataSet(FilenameC & ImageFile,FilenameC & FeatFile,IntT border)

Create a dataset from the features in feature files and using a double image

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SArray1dC<FARY *> GetFeatureImages(const FilenameC & ImageFile,const FilenameC & FeatureList)

Load in a FARY of feature images

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SArray1dC<FARY *> GetFeatureImages(const FilenameC & ImageFile,const DListC<StringC> & feats)

Load in a FARY of feature images

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void Save(ostream & out) const

Save the query to directory

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void SaveAsHtml(ostream & out,const StringC & QueryFile) const

Save the query as a html file

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NumVLDataSetC Data()

Non-const access to dataset held by object

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NumVLDataSetC Data() const

Const access to data

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void SetDataSet(NumVLDataSetC & in)

Change the stored dataset

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===================== Object Representation =====================