Package | Description |
---|---|
org.openimaj.audio | |
org.openimaj.audio.features | |
org.openimaj.feature | |
org.openimaj.feature.conversion | |
org.openimaj.image.analysis.algorithm | |
org.openimaj.image.annotation.evaluation.dataset | |
org.openimaj.image.connectedcomponent.proc | |
org.openimaj.image.feature | |
org.openimaj.image.feature.global | |
org.openimaj.image.model | |
org.openimaj.image.processing.face.detection |
The detection package contains implementations
of face detectors.
|
org.openimaj.image.processing.face.feature |
The feature package contains implementations of features
that can describe a face.
|
org.openimaj.lsh.functions | |
org.openimaj.math.statistics.distribution | |
org.openimaj.ml.clustering.spectral | |
org.openimaj.ml.kernel | |
org.openimaj.ml.pca | |
org.openimaj.text.nlp.sentiment.model.classifier | |
org.openimaj.video.processing.shotdetector | |
org.openimaj.vis.ternary | |
org.openimaj.workinprogress.featlearn |
Modifier and Type | Method and Description |
---|---|
abstract IncrementalAnnotator<DoubleFV,String> |
AudioAnnotator.AudioAnnotatorType.getAnnotator()
Returns a annotator that can train a DoubleFV feature with a specific
String label.
|
Modifier and Type | Method and Description |
---|---|
List<ScoredAnnotation<String>> |
AudioAnnotator.annotate(DoubleFV object)
Generate annotations for the given object.
|
Modifier and Type | Method and Description |
---|---|
void |
AudioAnnotator.train(Annotated<DoubleFV,String> annotated)
Train/update object using a new instance.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
JAudioFeatureExtractor.extractFeature(SampleChunk sc)
Calculates the feature for each channel, then flattens the channel arrays
into a single
DoubleFV . |
Modifier and Type | Class and Description |
---|---|
class |
MultidimensionalDoubleFV
Basic double multidimensional feature vector implementation
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
FeatureVector.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
ByteFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
SparseByteFV.asDoubleFV() |
DoubleFV |
FloatFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
SparseIntFV.asDoubleFV() |
DoubleFV |
IntFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
SparseFloatFV.asDoubleFV() |
DoubleFV |
LongFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
DoubleFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
SparseLongFV.asDoubleFV() |
DoubleFV |
SparseShortFV.asDoubleFV() |
DoubleFV |
ShortFV.asDoubleFV()
Convert the FV to a DoubleFV representation
|
DoubleFV |
EnumFV.asDoubleFV() |
DoubleFV |
SparseDoubleFV.asDoubleFV() |
DoubleFV |
DoubleFV.clone() |
DoubleFV |
DoubleFV.concatenate(DoubleFV... ins) |
DoubleFV |
DoubleFV.concatenate(List<DoubleFV> ins) |
DoubleFV |
DoubleFV.newInstance() |
DoubleFV |
FeatureVector.normaliseFV()
Normalise the FV to unit length
|
DoubleFV |
ByteFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
SparseByteFV.normaliseFV() |
DoubleFV |
FloatFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
SparseIntFV.normaliseFV() |
DoubleFV |
IntFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
SparseFloatFV.normaliseFV() |
DoubleFV |
LongFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
DoubleFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
SparseLongFV.normaliseFV() |
DoubleFV |
SparseShortFV.normaliseFV() |
DoubleFV |
ShortFV.normaliseFV()
Normalise the FV to unit area.
|
DoubleFV |
EnumFV.normaliseFV() |
DoubleFV |
SparseDoubleFV.normaliseFV() |
DoubleFV |
FeatureVector.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
ByteFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
SparseByteFV.normaliseFV(double p) |
DoubleFV |
FloatFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
SparseIntFV.normaliseFV(double p) |
DoubleFV |
IntFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
SparseFloatFV.normaliseFV(double p) |
DoubleFV |
LongFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
DoubleFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
SparseLongFV.normaliseFV(double p) |
DoubleFV |
SparseShortFV.normaliseFV(double p) |
DoubleFV |
ShortFV.normaliseFV(double p)
Lp Norm of the FV.
|
DoubleFV |
EnumFV.normaliseFV(double p) |
DoubleFV |
SparseDoubleFV.normaliseFV(double p) |
DoubleFV |
FeatureVector.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected minimum and
maximum values for each element of the underlying feature vector.
|
DoubleFV |
ByteFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
SparseByteFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
FloatFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
SparseIntFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
IntFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
SparseFloatFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
LongFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
DoubleFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
SparseLongFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
SparseShortFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
ShortFV.normaliseFV(double[] min,
double[] max)
Element-wise normalisation to 0..1 using separated expected
minimum and maximum values for each element of the underlying
feature vector.
|
DoubleFV |
EnumFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
SparseDoubleFV.normaliseFV(double[] min,
double[] max) |
DoubleFV |
FeatureVector.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
ByteFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
SparseByteFV.normaliseFV(double min,
double max) |
DoubleFV |
FloatFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
SparseIntFV.normaliseFV(double min,
double max) |
DoubleFV |
IntFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
SparseFloatFV.normaliseFV(double min,
double max) |
DoubleFV |
LongFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
DoubleFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
SparseLongFV.normaliseFV(double min,
double max) |
DoubleFV |
SparseShortFV.normaliseFV(double min,
double max) |
DoubleFV |
ShortFV.normaliseFV(double min,
double max)
Min-Max normalisation of the FV.
|
DoubleFV |
EnumFV.normaliseFV(double min,
double max) |
DoubleFV |
SparseDoubleFV.normaliseFV(double min,
double max) |
DoubleFV |
DoubleFV.subvector(int beginIndex) |
DoubleFV |
DoubleFV.subvector(int beginIndex,
int endIndex) |
Modifier and Type | Method and Description |
---|---|
double |
DoubleFVComparison.compare(DoubleFV h1,
DoubleFV h2) |
double |
DoubleFV.compare(DoubleFV h,
DoubleFVComparison method)
Compare this FV to another with the given method.
|
DoubleFV |
DoubleFV.concatenate(DoubleFV... ins) |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
DoubleFV.concatenate(List<DoubleFV> ins) |
Modifier and Type | Method and Description |
---|---|
static FloatFV |
FVConverter.toFloatFV(DoubleFV fv)
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
EdgeDirectionCoherenceVector.EdgeDirectionCoherenceHistogram.asDoubleFV()
Get the histogram (coherent followed by incoherent) as a double
vector.
|
DoubleFV |
EdgeDirectionCoherenceVector.getFeatureVector()
Get the FeatureVector associated with this object.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
Corel5kDataset.HistogramExtractor.extractFeature(org.openimaj.image.annotation.evaluation.dataset.ImageWrapper object) |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
BasicShapeDescriptor.getFeatureVector() |
DoubleFV |
HuMoments.getFeatureVector() |
DoubleFV |
ColourDescriptor.getFeatureVector() |
DoubleFV |
BoundaryDistanceDescriptor.getFeatureVector() |
DoubleFV |
AffineInvariantMoments.getFeatureVector() |
abstract DoubleFV |
BasicShapeDescriptor.BasicShapeDescriptorType.getFeatureVector(BasicShapeDescriptor desc)
Create a @link{FeatureVector} representation of the specified
description
|
abstract DoubleFV |
ColourDescriptor.ColourDescriptorType.getFeatureVector(ColourDescriptor desc)
Extract the feature for the given descriptor.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
FImage2DoubleFV.extractFeature(FImage object) |
Modifier and Type | Method and Description |
---|---|
FImage |
DoubleFV2FImage.extractFeature(DoubleFV object) |
static FImage |
DoubleFV2FImage.extractFeature(DoubleFV fv,
int width,
int height)
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
Naturalness.getFeatureVector() |
DoubleFV |
HueStats.getFeatureVector() |
DoubleFV |
RGBRMSContrast.getFeatureVector() |
DoubleFV |
ROIProportion.getFeatureVector() |
DoubleFV |
LuoSimplicity.getFeatureVector() |
DoubleFV |
SharpPixelProportion.getFeatureVector() |
DoubleFV |
Sharpness.getFeatureVector() |
DoubleFV |
RMSContrast.getFeatureVector() |
DoubleFV |
SaturationVariation.getFeatureVector() |
DoubleFV |
Saturation.getFeatureVector() |
DoubleFV |
SharpnessVariation.getFeatureVector() |
DoubleFV |
AvgBrightness.getFeatureVector() |
DoubleFV |
WeberContrast.getFeatureVector() |
DoubleFV |
RuleOfThirds.getFeatureVector() |
DoubleFV |
LRIntensityBalance.getFeatureVector() |
DoubleFV |
ColourContrast.getFeatureVector() |
DoubleFV |
ModifiedLuoSimplicity.getFeatureVector() |
DoubleFV |
HorizontalIntensityDistribution.getFeatureVector() |
DoubleFV |
Colorfulness.getFeatureVector() |
DoubleFV |
YehBokehEstimator.getFeatureVector() |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
EigenImages.extractFeature(FImage img) |
DoubleFV |
FisherImages.extractFeature(FImage object) |
Modifier and Type | Method and Description |
---|---|
FImage |
EigenImages.reconstruct(DoubleFV weights)
Reconstruct an image from a weight vector
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
CLMDetectedFace.getPoseParameters()
Get the parameters describing the pose of the face.
|
DoubleFV |
CLMDetectedFace.getPoseShapeParameters()
Get a vector describing the pose (pitch, yaw and roll only) and shape of
the model.
|
DoubleFV |
CLMDetectedFace.getShapeParameters()
Get the parameters describing the shape model (i.e.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
FisherFaceFeature.getFeatureVector() |
DoubleFV |
CLMPoseShapeFeature.getFeatureVector() |
DoubleFV |
CLMShapeFeature.getFeatureVector() |
DoubleFV |
EigenFaceFeature.getFeatureVector() |
DoubleFV |
CLMPoseFeature.getFeatureVector() |
Constructor and Description |
---|
CLMPoseFeature(DoubleFV fv)
Construct the
CLMPoseFeature with the given feature vector. |
CLMPoseShapeFeature(DoubleFV fv)
Construct the
CLMPoseShapeFeature with the given feature vector. |
CLMShapeFeature(DoubleFV fv)
Construct the
CLMShapeFeature with the given feature vector. |
EigenFaceFeature(DoubleFV fv)
Construct the EigenFaceFeature with the given feature vector.
|
FisherFaceFeature(DoubleFV fv)
Construct the FisherFaceFeature with the given feature vector.
|
Modifier and Type | Method and Description |
---|---|
int |
DoubleHashFunction.computeHashCode(DoubleFV feature)
Compute the hash code for the feature vector.
|
Modifier and Type | Class and Description |
---|---|
class |
Histogram
Simple Histogram based on a DoubleFV.
|
class |
MultidimensionalHistogram
Simple Histogram based on a MultidimensionalDoubleFV.
|
Constructor and Description |
---|
Histogram(DoubleFV... hs)
Construct a histogram by concatenating the given histograms
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
DummyExtractor.extractFeature(double[] object) |
Constructor and Description |
---|
DoubleFVSimilarityFunction(FeatureExtractor<DoubleFV,T> extractor) |
NormalisedSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV,T> extractor,
double eps) |
RBFSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV,T> extractor) |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
HomogeneousKernelMap.evaluate(DoubleFV in)
Compute the Homogeneous Kernel Map approximation of the given feature vector
|
DoubleFV |
HomogeneousKernelMap.ExtractorWrapper.extractFeature(T object) |
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. |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
HomogeneousKernelMap.evaluate(DoubleFV in)
Compute the Homogeneous Kernel Map approximation of the given feature vector
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
FeatureVectorPCA.generate(DoubleFV scalings)
Generate a new "observation" as a linear combination of the principal
components (PC): mean + PC * scaling.
|
DoubleFV |
FeatureVectorPCA.project(FeatureVector vector)
Project a vector by the basis.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
FeatureVectorPCA.generate(DoubleFV scalings)
Generate a new "observation" as a linear combination of the principal
components (PC): mean + PC * scaling.
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
GeneralSentimentFeatureExtractor.extractFeature(List<String> tokens) |
Modifier and Type | Method and Description |
---|---|
DoubleFV |
VideoShotDetector.getDifferentials()
Get the differentials between frames (if storeAllDiff is true).
|
Modifier and Type | Class and Description |
---|---|
static class |
TernaryPlot.TernaryData
Holds an a value for the 3 ternary dimensions and a value
|
Modifier and Type | Method and Description |
---|---|
DoubleFV |
TestImageClass.extractFeature(FImage bigpatch) |