Modifier and Type | Method and Description |
---|---|
LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> |
DoGSiftFeatureExtractor.apply(FImage img) |
LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> |
DSiftFeatureExtractor.apply(FImage img) |
Modifier and Type | Method and Description |
---|---|
MixtureOfGaussians |
GMMFromFeatures.apply(LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> features) |
Modifier and Type | Interface and Description |
---|---|
interface |
FeatureVectorProvider<T extends FeatureVector>
A FeatureVectorProvider marks classes that have an associated
FeatureVector or are capable of producing a FeatureVector from
their internal state.
|
interface |
FVComparator<T extends FeatureVector>
Interface for objects that can compare feature vectors
|
class |
FVProviderExtractor<FV extends FeatureVector,OBJECT>
An adaptor for
FeatureExtractor s that return objects that are
FeatureVectorProvider s that is a FeatureExtractor that
returns a FeatureVector . |
Modifier and Type | Class and Description |
---|---|
class |
ArrayFeatureVector<ARRAYTYPE>
Abstract base class for all types of
FeatureVector that are backed by
a native array. |
class |
ByteFV
Basic byte single-dimensional feature vector implementation
|
class |
DoubleFV
Basic double single-dimensional feature vector implementation
|
class |
EnumFV<T extends Enum<T>>
A feature-vector representation of an enumeration
|
class |
FloatFV
Basic float single-dimensional feature vector implementation
|
class |
IntFV
Basic int single-dimensional feature vector implementation
|
class |
LongFV
Basic long single-dimensional feature vector implementation
|
class |
MultidimensionalByteFV
Basic byte multidimensional feature vector implementation
|
class |
MultidimensionalDoubleFV
Basic double multidimensional feature vector implementation
|
class |
MultidimensionalFloatFV
Basic float multidimensional feature vector implementation
|
class |
MultidimensionalIntFV
Basic int multidimensional feature vector implementation
|
class |
MultidimensionalLongFV
Basic long multidimensional feature vector implementation
|
class |
MultidimensionalShortFV
Basic short multidimensional feature vector implementation
|
class |
OrientedFeatureVector
Byte featurevector with an associated orientation
|
class |
ShortFV
Basic short single-dimensional feature vector implementation
|
class |
SparseByteFV
A sparse one-dimensional feature vector of byte-valued elements.
|
class |
SparseDoubleFV
A sparse one-dimensional feature vector of double-valued elements.
|
class |
SparseFloatFV
A sparse one-dimensional feature vector of float-valued elements.
|
class |
SparseIntFV
A sparse one-dimensional feature vector of int-valued elements.
|
class |
SparseLongFV
A sparse one-dimensional feature vector of long-valued elements.
|
class |
SparseShortFV
A sparse one-dimensional feature vector of short-valued elements.
|
Modifier and Type | Method and Description |
---|---|
static <FV extends FeatureVector,OBJECT,EXTRACTOR extends FeatureExtractor<? extends FeatureVectorProvider<FV>,OBJECT>> |
FVProviderExtractor.create(EXTRACTOR extractor)
Create a new
FVProviderExtractor with the given extractor. |
Modifier and Type | Method and Description |
---|---|
FeatureVector |
FeatureVector.newInstance()
Construct a new instance of this featurevector.
|
Modifier and Type | Method and Description |
---|---|
static FloatFV |
FVConverter.toFloatFV(FeatureVector fv)
|
Modifier and Type | Interface and Description |
---|---|
interface |
LocalFeature<L extends Location,T extends FeatureVector>
A
LocalFeature models a feature that has a Location
associated with it. |
class |
LocalFeatureImpl<L extends Location,F extends FeatureVector>
A basic implementation of a
LocalFeature that internally holds
references to a FeatureVector and Location . |
interface |
LocalFeatureVectorProvider<L extends Location,T extends FeatureVector>
A
LocalFeatureVectorProvider models an object with both a
Location and a feature vector associated with it. |
Modifier and Type | Field and Description |
---|---|
F |
LocalFeatureImpl.feature
The
FeatureVector of the local feature |
Modifier and Type | Interface and Description |
---|---|
interface |
Normaliser<F extends FeatureVector>
Interface describing classes that can normalise a
FeatureVector . |
Modifier and Type | Class and Description |
---|---|
class |
ImageAnalyserFVFeatureExtractor<ANALYSER extends ImageAnalyser<IMAGE> & FeatureVectorProvider<FEATURE>,IMAGE extends Image<?,IMAGE>,FEATURE extends FeatureVector>
A
FeatureExtractor that wraps ImageAnalyser s that
can provide FeatureVector s through FeatureVectorProvider . |
Modifier and Type | Class and Description |
---|---|
class |
BlockSpatialAggregator<T,AGGREGATE extends FeatureVector & Concatenatable<AGGREGATE,AGGREGATE>>
A
BlockSpatialAggregator performs spatial pooling of local features
by grouping the local features into non-overlapping, fixed-size spatial
blocks, and applying a VectorAggregator (i.e. |
class |
PyramidSpatialAggregator<T,AGGREGATE extends FeatureVector & Concatenatable<AGGREGATE,AGGREGATE>>
A
PyramidSpatialAggregator performs spatial pooling of local features
by grouping the local features into fixed-size spatial blocks within a
pyramid, and applying a VectorAggregator (i.e. |
interface |
SpatialVectorAggregator<FEATURE extends FeatureVector,LOCATION extends Location,BOUNDS>
Interface describing an object that can convert a list of local features from
a single image into an aggregated vector form, using both the featurevector
and spatial location of each local feature.
|
interface |
VectorAggregator<FEATURE extends FeatureVector,AGGREGATE extends FeatureVector>
Interface describing an object that can convert a list of local features from
a single image into an aggregated vector form.
|
interface |
VectorAggregator<FEATURE extends FeatureVector,AGGREGATE extends FeatureVector>
Interface describing an object that can convert a list of local features from
a single image into an aggregated vector form.
|
Modifier and Type | Method and Description |
---|---|
FeatureVector |
SpatialVectorAggregator.aggregate(List<? extends LocalFeature<? extends LOCATION,? extends FEATURE>> features,
BOUNDS bounds)
Aggregate the given features into a vector.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LocalFeatureProvider<T extends FeatureVector>
Base interface for classes capable of building local descriptors.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ScaleSpaceFeatureExtractor<F extends FeatureVector,IMAGE extends Image<?,IMAGE> & SinglebandImageProcessor.Processable<Float,FImage,IMAGE>>
The ScaleSpaceImageExtractor interface describes a class capable of extracting
FeatureVector(s) from the information provided in a
ScaleSpaceImageExtractorProperties . |
Modifier and Type | Interface and Description |
---|---|
interface |
FeatureVectorExtractor<F extends FeatureVector,T extends ExtractorProperties>
The FeatureExtractor interface describes a class capable of extracting
FeatureVector(s) from something.
|
Modifier and Type | Method and Description |
---|---|
F[] |
FeatureVectorExtractor.extractFeature(T properties)
Extract FeatureVector(s), possibly using the information given in the
properties.
|
Modifier and Type | Method and Description |
---|---|
abstract <T extends Image<?,T>> |
FaceDetectorFeatures.getFeatureVector(List<? extends DetectedFace> faces,
T img)
Compute a feature vector describing the detections.
|
Modifier and Type | Class and Description |
---|---|
class |
FaceFVComparator<T extends FacialFeature & FeatureVectorProvider<Q>,Q extends FeatureVector>
A generic
FacialFeatureComparator for FacialFeature s that can
provide FeatureVector s through the FeatureVectorProvider
interface. |
Modifier and Type | Class and Description |
---|---|
class |
Histogram
Simple Histogram based on a DoubleFV.
|
class |
MultidimensionalHistogram
Simple Histogram based on a MultidimensionalDoubleFV.
|
Modifier and Type | Method and Description |
---|---|
static <OBJECT extends FeatureVector,ANNOTATION> |
NaiveBayesAnnotator.create(NaiveBayesAnnotator.Mode mode)
Convenience method to construct a
NaiveBayesAnnotator in the case
where the raw objects are themselves the feature and thus an
IdentityFeatureExtractor can be used. |
Constructor and Description |
---|
NaiveBayesAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor,
NaiveBayesAnnotator.Mode mode)
Construct a
NaiveBayesAnnotator with the given feature extractor
and mode of operation. |
Constructor and Description |
---|
DenseLinearTransformAnnotator(int k,
FeatureExtractor<? extends FeatureVector,OBJECT> extractor)
Construct with the given number of dimensions and feature extractor.
|
LiblinearAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor,
LiblinearAnnotator.Mode mode,
de.bwaldvogel.liblinear.SolverType solver,
double C,
double eps)
Default constructor.
|
LiblinearAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor,
LiblinearAnnotator.Mode mode,
de.bwaldvogel.liblinear.SolverType solver,
double C,
double eps,
double bias,
boolean dense)
Default constructor.
|
LinearSVMAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor)
Construct a new
LinearSVMAnnotator with the given extractor. |
LinearSVMAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor,
ANNOTATION negativeClass)
Construct a new
LinearSVMAnnotator with the given extractor and
the specified negative class. |
Constructor and Description |
---|
SVMAnnotator(FeatureExtractor<? extends FeatureVector,OBJECT> extractor)
Constructor that takes the feature extractor to use.
|
Modifier and Type | Method and Description |
---|---|
static de.bwaldvogel.liblinear.Feature[] |
LiblinearHelper.convert(FeatureVector feature,
double bias)
Convert a
FeatureVector to an array of Feature s. |
static double[] |
LiblinearHelper.convertDense(FeatureVector feature,
double bias)
Convert a
FeatureVector to an array of doubles using
asDoubleVector() . |
Modifier and Type | Class and Description |
---|---|
class |
FeatureVectorCentroidsResult<T extends FeatureVector>
The result of a
SpatialClusterer that just produces a flat set of
centroids in the form of FeatureVector s. |
Modifier and Type | Field and Description |
---|---|
T[] |
FeatureVectorCentroidsResult.centroids
The centroids of the clusters
|
Modifier and Type | Method and Description |
---|---|
T[] |
FeatureVectorCentroidsResult.getCentroids() |
Modifier and Type | Class and Description |
---|---|
class |
ExactFeatureVectorAssigner<T extends FeatureVector>
A
HardAssigner that assigns points to the closest cluster based on
the distance to the centroid. |
Modifier and Type | Method and Description |
---|---|
int[] |
ExactFeatureVectorAssigner.assign(T[] data) |
void |
ExactFeatureVectorAssigner.assignDistance(T[] data,
int[] indices,
float[] distances) |
Constructor and Description |
---|
ExactFeatureVectorAssigner(T[] data,
DistanceComparator<? super T> comparison)
Construct the assigner using the given cluster data and distance
function.
|
Modifier and Type | Class and Description |
---|---|
class |
FeatureVectorKMeans<T extends FeatureVector>
Fast, parallel implementation of the K-Means algorithm with support for
bigger-than-memory data.
|
static class |
FeatureVectorKMeans.Result<T extends FeatureVector>
Result object for FeatureVectorKMeans, extending
FeatureVectorCentroidsResult and ObjectNearestNeighboursProvider, as well
as giving access to state information from the operation of the K-Means
algorithm (i.e.
|
Modifier and Type | Method and Description |
---|---|
static <T extends FeatureVector> |
FeatureVectorKMeans.createExact(int K,
DistanceComparator<? super T> distance)
Convenience method to quickly create an exact
ByteKMeans . |
static <T extends FeatureVector> |
FeatureVectorKMeans.createExact(int K,
DistanceComparator<? super T> distance,
int niters)
Convenience method to quickly create an exact
ByteKMeans . |
Modifier and Type | Method and Description |
---|---|
FeatureVectorKMeans.Result<T> |
FeatureVectorKMeans.cluster(T[] data) |
void |
FeatureVectorKMeans.cluster(T[] data,
FeatureVectorKMeans.Result<T> result)
Main clustering algorithm.
|
int[][] |
FeatureVectorKMeans.performClustering(T[] data) |
Modifier and Type | Method and Description |
---|---|
<T> FeatureExtractor<DoubleFV,T> |
HomogeneousKernelMap.createWrappedExtractor(FeatureExtractor<? extends FeatureVector,T> inner)
Construct a new
HomogeneousKernelMap.ExtractorWrapper that applies the map to features
extracted by an internal extractor. |
Constructor and Description |
---|
ExtractorWrapper(FeatureExtractor<? extends FeatureVector,T> inner,
HomogeneousKernelMap map)
Construct with the given internal extractor and homogeneous kernel map.
|
Modifier and Type | Method and Description |
---|---|
void |
FeatureVectorPCA.learnBasis(FeatureVector[] data)
Learn the PCA basis of the given feature vectors.
|
DoubleFV |
FeatureVectorPCA.project(FeatureVector vector)
Project a vector by the basis.
|
Modifier and Type | Method and Description |
---|---|
void |
FeatureVectorPCA.learnBasis(Collection<? extends FeatureVector> data)
Learn the PCA basis of the given feature vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
Document
A document is a bag of words
|
Modifier and Type | Method and Description |
---|---|
List<? extends FeatureVectorProvider<? extends FeatureVector>> |
LocalColourFeatureMode.extractFeatures(File imageFile) |
List<? extends FeatureVectorProvider<? extends FeatureVector>> |
TrendDetectorFeatureExtractor.extractFeatures(File imageFile) |
Modifier and Type | Method and Description |
---|---|
protected <T extends FeatureVector> |
SegmentingPairWiseComparisonTool.getComp(T fv,
FeatureComparison type) |
protected <T extends FeatureVector> |
PairWiseComparisonTool.getComp(T fv,
FeatureComparison type) |
protected <T extends FeatureVector> |
CollectionComparisonTool.getComp(T fv,
FeatureComparison type)
Get a feature comparison class for the given feature and metric.
|
Modifier and Type | Method and Description |
---|---|
FeatureVector |
ShapeFeatures.ShapeFeaturesOp.execute(MBFImage image,
FImage mask)
Calculate a feature using the shape defined by the mask and possibly
the pixel values from the image.
|
FeatureVector |
GlobalFeatureExtractor.extract(MBFImage image)
Create the feature from the image.
|
abstract FeatureVector |
GlobalFeatureExtractor.extract(MBFImage image,
FImage mask)
Create the feature from the image and optional mask.
|
Modifier and Type | Method and Description |
---|---|
FeatureVector |
WeberContrastExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
HaarFacesExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
SharpnessExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
LocalHistogramExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
RuleOfThirdsExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
HueStatsExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
EDCHExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
RoiProportionExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
ModifiedLuoSimplicityExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
MaxHistogramExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
HistogramExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
SharpPixelProportionExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
ColourfulnessExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
LrIntensityBalanceExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
ColourContrastExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
AverageBrightnessExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
HorizontalIntensityDistributionExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
ColourFacesExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
NaturalnessExtractor.extract(MBFImage image,
FImage mask) |
FeatureVector |
LuoSimplicityExtractor.extract(MBFImage image,
FImage mask) |
Modifier and Type | Class and Description |
---|---|
static class |
TernaryPlot.TernaryData
Holds an a value for the 3 ternary dimensions and a value
|