Package | Description |
---|---|
org.openimaj.demos | |
org.openimaj.experiment.gmm.retrieval | |
org.openimaj.image.feature.local.aggregate |
Implementations of techniques that aggregate the local descriptors
of an image into a single (typically fixed length) vector
representation.
|
org.openimaj.ml.gmm |
Modifier and Type | Method and Description |
---|---|
static MixtureOfGaussians |
FVFWCheckPCAGMM.loadMoG(File f) |
Modifier and Type | Method and Description |
---|---|
MixtureOfGaussians |
GMMFromFeatures.apply(LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> features) |
protected MixtureOfGaussians |
UKBenchGMMExperiment.extract(org.openimaj.experiment.gmm.retrieval.UKBenchGMMExperiment.IRecord<URL> item) |
Modifier and Type | Method and Description |
---|---|
Map<Integer,List<MixtureOfGaussians>> |
UKBenchGMMExperiment.extractGroupGaussians() |
List<MixtureOfGaussians> |
UKBenchGMMExperiment.extractGroupGaussians(int i) |
List<MixtureOfGaussians> |
UKBenchGMMExperiment.extractGroupGaussians(UKBenchListDataset<org.openimaj.experiment.gmm.retrieval.UKBenchGMMExperiment.IRecord<URL>> ukbenchObject) |
Constructor and Description |
---|
FisherVector(MixtureOfGaussians gmm)
Construct the standard Fisher Vector encoder with the given mixture of
Gaussians.
|
FisherVector(MixtureOfGaussians gmm,
boolean improved)
Construct the Fisher Vector encoder with the given mixture of Gaussians
and the optional improvement steps (in the sense of the VLFeat
documentation).
|
FisherVector(MixtureOfGaussians gmm,
boolean hellinger,
boolean l2normalise)
Construct with the given mixture of Gaussians and optional improvement
steps.
|
Modifier and Type | Class and Description |
---|---|
protected static class |
GaussianMixtureModelEM.EMGMM |
Modifier and Type | Method and Description |
---|---|
MixtureOfGaussians |
GaussianMixtureModelEM.estimate(double[][] X)
Estimate a new
MixtureOfGaussians from the given data. |
MixtureOfGaussians |
GaussianMixtureModelEM.estimate(Jama.Matrix X)
Estimate a new
MixtureOfGaussians from the given data. |