Class Hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12345]

drwnPersistentStorageBuffer< T >::_drwnRecordEntry
drwnDisjointSets::_node_t
binary_function
drwnNNGraphSortByImage
drwnNNGraphSortByScore
drwnBitArray	Implements an efficient packed array of bits
drwnClassificationResults	Encapsulates summary of classifier output from which various curves can be generated (e.g., precision-recall curves)
drwnPRCurve	Precision-recall curve
drwnCloneable	Interface for cloning object (i.e., virtual copy constructor)
drwnFeatureMap	Defines the interface for a feature mapping
drwnBiasFeatureMap	Augments input feature vector with 1 (i.e., to allow for a bias weight)
drwnIdentityFeatureMap	Copies input feature space to output feature space
drwnQuadraticFeatureMap	Augments input feature vector with square of each feature (normalized so that if input is zero mean and unit variance so will output) as well as cross-terms and constant one (i.e., to allow for a bias weight)
drwnSquareFeatureMap	Augments input feature vector with square of each feature (normalized so that if input is zero mean and unit variance so will output) and 1 (i.e., to allow for a bias weight)
drwnJointFeatureMap	Defines the interface for a joint feature mapping
drwnBiasJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a bias term for each class i.e.,
drwnIdentityJointFeatureMap	Includes a copy of each feature from the input space for each class other than the last, i.e., . This is the standard feature mapping for multi-class logistic models
drwnQuadraticJointFeatureMap	Same as drwnSquareJointFeatureMap but adds cross-terms
drwnSquareJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a square term for each feature i.e.,
drwnSegImagePixelFeatures	Interface for generating per-pixel features for a drwnSegImageInstance object
drwnSegImageCompositePixelFeatures	Class for generating composite per-pixel feature vectors
drwnSegImageFilePixelFeatures	Pre-processed per-pixel features stored in files
drwnSegImageStdPixelFeatures	Standard per-pixel filterbank features with option to read auxiliary features from a file
drwnSegImageRegionFeatures	Interface for generating per-region (or per-superpixel) features for a drwnSegImageInstance object. The superpixel data member of the drwnSegImageInstance object must be populated
drwnSegImageStdRegionFeatures	Standard per-region filterbank features computes mean and standard deviation of drwnTextonFilterBank responses over each region
drwnStdObjIface	Standard Darwin object interface (cloneable and writeable)
drwnClassifier	Implements the interface for a generic machine learning classifier
drwnBoostedClassifier	Implements a mult-class boosted decision-tree classifier. See Zhu et al., Multi-class AdaBoost, 2006
drwnCompositeClassifier	Implements a multi-class classifier by combining binary classifiers
drwnDecisionTree	Implements a (binary-split) decision tree classifier of arbitrary depth
drwnMultiClassLogisticBase	Common functionality for drwnMultiClassLogistic
drwnTMultiClassLogistic< FeatureMap >	Implements a multi-class logistic classifier templated on a drwnJointFeatureMap
drwnTMultiClassLogistic< drwnBiasJointFeatureMap >
drwnRandomForest	Implements a Random forest ensemble of decision trees classifier. See L. Breiman, "Random Forests", Machine Learning, 2001
drwnColourHistogram	Specialized histogram for quantized 3-channel colour values (e.g., RGB)
drwnConditionalGaussian	Utility class for generating conditonal gaussian distribution
drwnCondSuffStats	Implements a class for accumulating conditional first- and second-order sufficient statistics
drwnDeformationCost	Structure for holding dx, dy, dx^2 and dy^2 deformation costs
drwnFactor	Generic interface for a factor. Currently only inherited by drwnTableFactor
drwnTableFactor	Factor which stores the value of each assignment explicitly in table form
drwnFactorGraph	Container and utility functions for factor graphs
drwnFeatureTransform	Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised)
drwnLinearTransform	Implements a linear feature transform with externally settable parameters
drwnSupervisedTransform	Implements interface for supervised feature transforms (i.e., with class labels)
drwnFisherLDA	Fisher's linear discriminant analysis (LDA)
drwnTFeatureMapTransform< FeatureMap >	Helper feature transformation based on a drwnFeatureMap
drwnUnsupervisedTransform	Implements interface for unsupervised feature transforms (i.e, without class labels)
drwnFeatureWhitener	Whitens (zero mean, unit variance) feature vector (see also drwnPCA)
drwnKMeans	Implements k-means clustering. Outputs the squared-distance to each of the cluster centroids. The nearest cluster can be found by passing the output to the drwn::argmin function. Supports weighted training examples
drwnPCA	Principal component analysis feature transformation
drwnGaussian	Implements a multi-variate gaussian distribution
drwnGaussianMixture	Implements a multi-variant Gaussian mixture model
drwnPart	A part is defined as a template over a number of channels (possibly one), an offset from the object centroid, and a deformation cost. The part is scored as w^T F(x) where w are the template weights, and F(x) is the feature vector at location x in feature space. Note for edge templates this can be in pixels, but generally will be in some multiple of pixels. It is up to the inference model to handle conversion from feature space to pixel space
drwnPartsModel	Interface for implementing a part-based constellation model (i.e., pictorial structures model) for object detection
drwnHOGPartsModel
drwnTemplatePartsModel
drwnPixelNeighbourContrasts	Convenience class for holding pixel contrast weights
drwnRegression	Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor
drwnLinearRegressorBase	Common functionality for drwnLinearRegressor
drwnTLinearRegressor< FeatureMap >	Implements linear regression optimization templated on a drwnFeatureMap
drwnSuffStats	Implements a class for accumulating first- and second-order sufficient statistics (moments)
drwnVarUniverse	Data structure for definining the random variables (name and cardinality) for a given problem instance
drwnCodeProfiler	Static class for providing profile information on functions
drwnCompressionBuffer	Utility class for compressing data using the zlib library
drwnConfigurableModule	Interface for a configurable module
drwnADLPInferenceConfig
drwnBoostedClassifierConfig
drwnCodeProfilerConfig
drwnCompositeClassifierConfig
drwnConfusionMatrixConfig
drwnDecisionTreeConfig
drwnGaussianConfig
drwnGaussianMixtureConfig
drwnGrabCutConfig
drwnHOGFeaturesConfig
drwnImageCacheConfig
drwnImageInPainterConfig
drwnImagePyramidCacheConfig
drwnKMeansConfig
drwnLinearRegressorConfig
drwnLoggerConfig
drwnMAPInferenceConfig
drwnMaskedPatchMatchConfig
drwnMultiClassLogisticConfig
drwnMultiSegConfig	Manages configuration settings for multiple image segmentation
drwnNNGraphConfig
drwnNNGraphLearnerConfig
drwnOpenCVUtilsConfig
drwnPartsModelConfig
drwnPatchMatchConfig
drwnRandomForestConfig
drwnSegImagePixelFeaturesConfig
drwnSegImageRegionFeaturesConfig
drwnThreadPoolConfig
drwnTRWSInferenceConfig
drwnXMLUtilsConfig
drwnConfigurationManager	Configuration manager
drwnConfusionMatrix	Utility class for computing and printing confusion matrices
drwnDataset< XType, YType, WType >	Implements a cacheable dataset containing feature vectors, labels and optional weights
drwnDisjointSets	Implements a forest of disjoint sets abstract data type
drwnFactorOperation	Base class for implementing various operations on table factors. The derived classes store mappings between factor entries making them very fast for iterative algorithms
drwnFactorAtomicOp	Executes an atomic operation by executing a sequence of factor operations
drwnFactorBinaryOp	Base class for implementing binary factor operations
drwnFactorDivideOp	Divide one factor by another
drwnFactorSubtractOp	Subtract one factor from another
drwnFactorWeightedSumOp	Add a weighted combination of factors
drwnFactorCopyOp	Copy one factor onto another
drwnFactorExpAndNormalizeOp	Exponentiate and normalize all the entries in a factor to sum to one
drwnFactorLogNormalizeOp	Shift all the entries in a factor so that the maximum is zero
drwnFactorMarginalizeOp	Marginalize out one or more variables in a factor
drwnFactorMaximizeOp	Maximize over one or more variables in a factor
drwnFactorMinimizeOp	Minimize over one or more variables in a factor
drwnFactorNAryOp	Base class for implementing n-ary factor operations
drwnFactorAdditionOp	Add two or more factors together
drwnFactorProductOp	Multiply two or more factors together
drwnFactorNormalizeOp	Normalize all the entries in a factor to sum to one. Assumes non-negative entries
drwnFactorReduceOp	Reduce factor by oberving the value of one or more variables
drwnFactorUnaryOp	Base class for implementing unary factor operations
drwnFactorMinusEqualsOp	Subtract one (weighted) factor from another inline
drwnFactorPlusEqualsOp	Add one (weighted) factor to another inline
drwnFactorTimesEqualsOp	Multiply one factor by another inline
drwnFactory< U >	Templated factory for creating or cloning objects for a particular base class
drwnFactoryAutoRegister< U, T >	Helper class for registering classes with a drwnFactory
drwnFactoryTraits< T >	Some classes may provide default factory registration (e.g., built-in classes such as drwnClassifier and drwnFeatureTransform)
drwnFactoryTraits< drwnClassifier >	Implements factory for classes derived from drwnClassifier with automatic registration of built-in classes
drwnFactoryTraits< drwnFeatureTransform >	Implements factory for classes derived from drwnFeatureTransform with automatic registration of built-in classes
drwnFilterBankResponse	Holds the results of running an image through a bank of filters and allows for computation of features over rectangular regions
drwnGrabCutInstance	Implements the grabCut algorithm of Rother et al., SIGGRAPH 2004 for figure/ground segmentation
drwnGrabCutInstanceGMM
drwnGrabCutInstanceHistogram
drwnHistogram< TYPE >	Implements a simple templated histogram class
drwnHOGFeatures	Encapsulates histogram-of-gradient (HOG) feature computation
drwnIconFactory
drwnImageCache	Caches images in memory up to a maximum number of images or memory limit
drwnImageInPainter	Performs exemplar-based image inpainting
drwnImagePyramidCache	Caches image pyramids in main memory up to a maximum number of images or memory limit
drwnIndexQueue	Provides a queue datastructure on a fixed number of indexes. At most one copy of each index can appear in the queue (a second enqueue is ignored). Membership of the queue can be queried
drwnInference	Interface for various (marginal) inference algorithms
drwnMessagePassingInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
drwnSumProdInference	Implements sum-product inference
drwnAsyncSumProdInference	Implements asynchronous sum-product inference
drwnLBPFilterBank	Implements filter bank for encoding local binary patterns
drwnLogger	Message and error logging. This class is not thread-safe in the interest of not having to flush the log on every message
drwnLPSolver	Solves equality constrained linear programs with positivity constraints via the log-barrier method
drwnMAPInference	Interface for various MAP inference (energy minimization) algorithms
drwnADLPInference	Implements the alternating direction method algorithm described in "An Alternating Direction Method for Dual MAP LP Relaxation," Ofer Meshi and Amir Globerson, ECML, 2011
drwnAlphaBetaSwapInference	Implements alpha-beta swap inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise
drwnAlphaExpansionInference	Implements alpha-expansion inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise
drwnDualDecompositionInference	Implements dual decomposition MAP inference (see Komodakis and Paragios, CVPR 2009 and works cited therein). Each factor is treated as a separate slave
drwnICMInference	Implements iterated conditional modes (ICM) MAP inference. This method was first proposed in Besag, Royal Stats Society, 1986
drwnJunctionTreeInference	Implements the junction tree algorithm for exact inference on a factor graph using drwnAsyncMaxProdInference for the actual message passing
drwnMessagePassingMAPInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
drwnAsyncMaxProdInference	Implements asynchronous max-product (min-sum) inference
drwnGEMPLPInference	Implements the generalized LP-based message passing algorithm of Globerson and Jaakkola, NIPS 2007
drwnSontag08Inference	Implements the incremental tightening of the LP MAP inference algorithm from Sontag et al., UAI 2008
drwnMaxProdInference	Implements max-product inference
drwnTRWSInference	Implements the sequential tree-reweighted message passing (TRW-S) algorithm described in "Convergent Tree-Reweighted Message Passing for Energy Minimization," Kolmogorov, IEEE PAMI, 2006
drwnMAPInferenceFactory	Factory for creating drwnMAPInference objects
drwnMaskedPatchMatch	Implements the basic PatchMatch algorithm of Barnes et al., SIGGRAPH 2009 on masked images
drwnMaxFlow	Interface for maxflow/min-cut algorithms (for minimizing submodular quadratic pseudo-Boolean functions)
drwnBKMaxFlow	Implementation of Boykov and Kolmogorov's maxflow algorithm
drwnEdmondsKarpMaxFlow	Implementation of Edmonds-Karp maxflow/min-cut algorithm
drwnMouseState	Mouse state and mouse callback for populating the mouse state. Used by the drwnWaitMouse function
drwnMultiSegRegionDefinitions	Provides a mechanism for mapping region IDs to colours and class names. Can be initialized from an XML configuration file or programmatically for a number of standard datasets
drwnNNGraph	Class for maintaining a nearest neighbour graph over superpixel images. Search moves are implemented by templated functions in the drwnNNGraphMoves namespace
drwnNNGraphDefaultMetric	Implements the scoring functions needed by the search moves. Required member functions are isMatchable(), isFinite() and score()
drwnNNGraphEdge	Encapsulates an outgoing edge in a drwnNNGraph
drwnNNGraphImageData	Holds image, segments and other housekeeping information for an image
drwnNNGraphLabelsEqualMetric
drwnNNGraphLabelsEqualMetricNoUnknown
drwnNNGraphLabelsNotEqualMetric
drwnNNGraphLabelsNotEqualMetricNoUnknown
drwnNNGraphLearner	Learn the distance metric base class with full set of constraints (i.e., loss function over all targets and imposters)
drwnNNGraphLLearner	Learn the distance metric M = LL^T as L^T
drwnNNGraphMLearner
drwnNNGraphSparseLearner	Learn the distance metric base class with sparse set of constraints (i.e., loss function over further target and nearest imposter only)
drwnNNGraphLSparseLearner
drwnNNGraphNodeAnnotation< T >	Templated utility class for holding annotations for every node in a graph. See learning code for example use
drwnNNGraphNodeAnnotation< drwnNNGraphLearnViolatedConstraints >
drwnNNGraphNodeAnnotation< drwnTriplet >
drwnNNGraphNodeIndex
drwnObject	Encapsulates a 2D object in an image for object detection
drwnOptimizer	Interface for solving large-scale unconstrained optimization problems using L-BFGS
drwnLinearRegressorBase	Common functionality for drwnLinearRegressor
drwnMultiClassLogisticBase	Common functionality for drwnMultiClassLogistic
drwnOrderedMap< KeyType, ValueType >	Provides a datastructure for that can be indexed by a KeyType (usually a string) or unsigned integer, i.e., the index
drwnPartsAssignment	Class for holding as assignment to part locations and occlusion variables
drwnPartsInference	Helper class for running inference in a (constellation) parts-based model. Supports linear and quadratic distance transforms for deformation costs. Computes argmax_{x,c,z} m_i(x_i, z_i) + d_i(x_i, c) + p(c) where m_i(x, 0) = matchingCost_i(x) + priorCost_i(x) m_i(x, 1) = occlusionCost + priorCost_i(x) d_i(x, c) = [dx, dy]^T fabs(x - o - c) + [dx2, dy2]^T (x - o - c).^2
drwnPartsModelMixture< T >	Mixture of parts models (T must have a parts model interface). Inference returns the best scoring model and its parts locations
drwnPatchMatchEdge	Represents an edge in the drwnPatchMatchGraph
drwnPatchMatchGraph	Each image maintains a W-by-H-by-K array of match records referencing the (approximate) best K matches to other images. Image filename rather than the images themselves are stored. Duplicate filenames (images) are not allowed
drwnPatchMatchGraphLearner	Learns a PatchMatchGraph by iteratively performing search moves over the space of matches
drwnPatchMatchGraphRepaint	Class for repainting an image from matches within the PatchMatchGraph
drwnPatchMatchImageRecord	Records matches for one level in an image pyramid
drwnPatchMatchNode	Represents a node in the drwnPatchMatchGraph
drwnPersistentBlock	Persistent storage block used internally by drwnPersistentStorage
drwnPersistentRecord	Interface class for drwnPersistentStorage
drwnNNGraphImage	Holds nodes (superpixels) for a single image
drwnNNGraphNode	Encapsulates a superpixel node in a drwnNNGraph
drwnPatchMatchImagePyramid	Record of patch matches for mutliple levels each image
drwnPersistentVectorRecord< T >	Templated class for storing vector records
drwnPersistentVectorVectorRecord< T >	Templated class for storing vector-of-vector records
drwnSuperpixelContainer	Holds multiple oversegmentations for a given image
drwnPersistentStorage	Provides indexed storage for multiple records using two files (a binary data file and a text index file)
drwnPersistentStorageBuffer< T >	Provides buffered storage (delayed write-back) of objects with a drwnPersistentRecord interface
drwnPixelSegCRFInference	Alpha-expansion inference for a pixel-level CRF model with unary, contrast-dependent pairwise, and custom higher-order terms
drwnRobustPottsCRFInference	And where 0.0 < < 0.5
drwnWeightedRobustPottsCRFInference	And where 0.0 < < 0.5, W_c = w_i
drwnProperties	Provides an abstract interface for dynamic properties
drwnClassifier	Implements the interface for a generic machine learning classifier
drwnFeatureTransform	Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised)
drwnMultiSegConfig	Manages configuration settings for multiple image segmentation
drwnPropertiesCopy
drwnRegression	Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor
drwnPropertyInterface
drwnStoragePropertyInterface< T >
drwnStoragePropertyInterface< bool >
drwnBooleanProperty
drwnStoragePropertyInterface< double >
drwnDoubleProperty
drwnDoubleRangeProperty
drwnStoragePropertyInterface< Eigen::MatrixXd >
drwnMatrixProperty
drwnStoragePropertyInterface< Eigen::VectorXd >
drwnVectorProperty
drwnStoragePropertyInterface< int >
drwnIntegerProperty
drwnRangeProperty
drwnSelectionProperty
drwnStoragePropertyInterface< list< string > >
drwnListProperty
drwnStoragePropertyInterface< string >
drwnStringProperty
drwnDirectoryProperty
drwnFilenameProperty
drwnQPSolver	Quadratic program solver
drwnLogBarrierQPSolver
drwnSegImageInstance	Encapsulates a single instance of an image for multi-class pixel labeling problems (i.e., image segmentation)
drwnSLICCentroid
drwnSmartPointer< T >	Implements a shared pointer interface to avoid the need to deep copy constant (shared) objects
drwnSmartPointer< drwnVarUniverse >
drwnSmartPointerCmpLessThan< T >	Comparison operator for objects held in a drwnSmartPointer
drwnSparseLPSolver	Solves linear programs with sparse equality constraints
drwnSparseVec< T >	Quick-and-dirty sparse vector class as a plugin replacement for std::vector
drwnTableFactorMapping	Creates a mapping between entries in two tables
drwnTableFactorStorage	Shared memory for table factors
drwnTemplateMatcher	Utility class for computing multiple template matches
drwnTextonFilterBank	Implements a 17-dimensional filter bank
drwnThreadJob	Interface for a thread job functor
drwnDecisionTreeThread
drwnGaussianMixtureThread
drwnNNGraphAppendImageJob
drwnNNGraphDataMatrixThread
drwnNNGraphMoveUpdateThread< DistanceMetric >
drwnNNGraphProjectFeaturesThread
drwnNNGraphSparseSubGradientThread
drwnNNGraphSubGradientThread
drwnNNGraphThreadedMoves::drwnNNGraphThreadedExhaustiveJob< DistanceMetric >	Threading exhaustive functor
drwnNNGraphThreadedMoves::drwnNNGraphThreadedInitializeJob< DistanceMetric >	Threading initialize functor
drwnNNGraphThreadedMoves::drwnNNGraphThreadedRescoreJob< DistanceMetric >	Threading rescore functor
drwnNNGraphThreadedMoves::drwnNNGraphThreadedUpdateJob< DistanceMetric >	Threading update functor
drwnPatchMatchThreadedInitialize
drwnPatchMatchThreadedUpdate
GridSearchJob
InferenceThread
labelTransferJob
loadDataJob
loadDataJob
drwnThreadPool	Implements a pool of threads for running concurrent jobs
drwnTriplet< T, U, V >	Basic datatype for holding three objects of arbitrary type. Similar to the STL pair<> class
drwnTypeable	Interface for an object that returns its own type as a string
drwnFeatureMap	Defines the interface for a feature mapping
drwnJointFeatureMap	Defines the interface for a joint feature mapping
drwnWriteable	Interface for objects that can serialize and de-serialize themselves
drwnCrossValidator	Utility class for cross-validating classifier meta-parameters by brute-force testing of all combinations of some given settings
drwnObjectList	List of objects for the same image (see drwnObject)
drwnObjectSequence	Sequence of images, each with a list of objects (see drwnObjectList)
drwnPixelSegModel	Implements a pixel-level CRF model for multi-class image segmentation (pixel labeling)
drwnStdObjIface	Standard Darwin object interface (cloneable and writeable)
drwnWeightedEdge
drwnWeightedPixelEdge	Weighted undirected arc between pixels in an image
drwnSparseVec< T >::iterator
drwnTableFactorMapping::iterator	Iterator for indexing entries in the tables
drwnXMLUtils::load_node< T >	Helper class for loading xml nodes into containers of objects
drwnXMLUtils::load_node< T * >	Helper class for (creating and) loading xml nodes into containers of pointers to objects
map
drwnObjectSequence	Sequence of images, each with a list of objects (see drwnObjectList)
drwnPartialAssignment	Defines an assignment to a subset of the variables
drwnSparseVec< T >::reference
drwnXMLUtils::save_node< T >	Helper class for saving objects to xml nodes
drwnXMLUtils::save_node< T * >	Helper class for saving pointers to objects to xml nodes
vector
drwnObjectList	List of objects for the same image (see drwnObject)
wxApp
DarwinApp
wxDialog
drwnMatrixEditor
drwnOptionsEditor
drwnTextEditor
wxFrame
MainWindow
wxScrolledWindow
MainCanvas
wxStatusBar
drwnStatusBar
wxWindow
drwnProgressBar