Uses of Interface
org.openimaj.ml.clustering.CentroidsProvider

Packages that use CentroidsProvider

Package	Description
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.clustering
org.openimaj.ml.clustering.assignment.hard
org.openimaj.ml.clustering.assignment.soft
org.openimaj.ml.clustering.kmeans	K-Means in OpenIMAJ is designed to be both extremely fast and flexible.
org.openimaj.ml.clustering.rac

Uses of CentroidsProvider in org.openimaj.image.feature.local.aggregate

Constructors in org.openimaj.image.feature.local.aggregate with parameters of type CentroidsProvider

Constructor and Description
VLAD(HardAssigner<T,?,?> assigner, CentroidsProvider<T> centroids, boolean normalise) Construct with the given assigner and the centroids associated with the assigner.

Uses of CentroidsProvider in org.openimaj.ml.clustering

Classes in org.openimaj.ml.clustering that implement CentroidsProvider

Modifier and Type	Class and Description
class	ByteCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.
class	DoubleCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.
class	FeatureVectorCentroidsResult<T extends FeatureVector> The result of a SpatialClusterer that just produces a flat set of centroids in the form of FeatureVectors.
class	FloatCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.
class	IntCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.
class	LongCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.
class	ShortCentroidsResult The result of a SpatialClusterer that just produces a flat set of centroids.

Uses of CentroidsProvider in org.openimaj.ml.clustering.assignment.hard

Constructors in org.openimaj.ml.clustering.assignment.hard with parameters of type CentroidsProvider

Constructor and Description
ExactByteAssigner(CentroidsProvider<byte[]> provider) Construct the assigner using the given cluster data.
ExactByteAssigner(CentroidsProvider<byte[]> provider, ByteFVComparator comparison) Construct the assigner using the given cluster data and distance function.
ExactDoubleAssigner(CentroidsProvider<double[]> provider) Construct the assigner using the given cluster data.
ExactDoubleAssigner(CentroidsProvider<double[]> provider, DoubleFVComparator comparison) Construct the assigner using the given cluster data and distance function.
ExactFeatureVectorAssigner(CentroidsProvider<T> provider, DistanceComparator<? super T> comparison) Construct the assigner using the given cluster data and distance function.
ExactFloatAssigner(CentroidsProvider<float[]> provider) Construct the assigner using the given cluster data.
ExactFloatAssigner(CentroidsProvider<float[]> provider, FloatFVComparator comparison) Construct the assigner using the given cluster data and distance function.
ExactIntAssigner(CentroidsProvider<int[]> provider) Construct the assigner using the given cluster data.
ExactIntAssigner(CentroidsProvider<int[]> provider, IntFVComparator comparison) Construct the assigner using the given cluster data and distance function.
ExactLongAssigner(CentroidsProvider<long[]> provider) Construct the assigner using the given cluster data.
ExactLongAssigner(CentroidsProvider<long[]> provider, LongFVComparator comparison) Construct the assigner using the given cluster data and distance function.
ExactShortAssigner(CentroidsProvider<short[]> provider) Construct the assigner using the given cluster data.
ExactShortAssigner(CentroidsProvider<short[]> provider, ShortFVComparator comparison) Construct the assigner using the given cluster data and distance function.
KDTreeByteEuclideanAssigner(CentroidsProvider<byte[]> provider) Construct the assigner using the given cluster data.
KDTreeDoubleEuclideanAssigner(CentroidsProvider<double[]> provider) Construct the assigner using the given cluster data.
KDTreeFloatEuclideanAssigner(CentroidsProvider<float[]> provider) Construct the assigner using the given cluster data.
KDTreeIntEuclideanAssigner(CentroidsProvider<int[]> provider) Construct the assigner using the given cluster data.
KDTreeLongEuclideanAssigner(CentroidsProvider<long[]> provider) Construct the assigner using the given cluster data.
KDTreeShortEuclideanAssigner(CentroidsProvider<short[]> provider) Construct the assigner using the given cluster data.

Uses of CentroidsProvider in org.openimaj.ml.clustering.assignment.soft

Fields in org.openimaj.ml.clustering.assignment.soft with type parameters of type CentroidsProvider

Modifier and Type	Field and Description
protected Map<CentroidsProvider<int[]>,HardAssigner<int[],float[],IntFloatPair>>	HierarchicalIntPathAssigner.assigners
protected Map<CentroidsProvider<float[]>,HardAssigner<float[],float[],IntFloatPair>>	HierarchicalFloatPathAssigner.assigners
protected Map<CentroidsProvider<byte[]>,HardAssigner<byte[],float[],IntFloatPair>>	HierarchicalBytePathAssigner.assigners
protected Map<CentroidsProvider<long[]>,HardAssigner<long[],double[],IntDoublePair>>	HierarchicalLongPathAssigner.assigners
protected Map<CentroidsProvider<double[]>,HardAssigner<double[],double[],IntDoublePair>>	HierarchicalDoublePathAssigner.assigners
protected Map<CentroidsProvider<short[]>,HardAssigner<short[],float[],IntFloatPair>>	HierarchicalShortPathAssigner.assigners

Constructors in org.openimaj.ml.clustering.assignment.soft with parameters of type CentroidsProvider

Constructor and Description
ByteKNNAssigner(CentroidsProvider<byte[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
ByteKNNAssigner(CentroidsProvider<byte[]> provider, ByteFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.
DoubleKNNAssigner(CentroidsProvider<double[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
DoubleKNNAssigner(CentroidsProvider<double[]> provider, DoubleFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.
FloatKNNAssigner(CentroidsProvider<float[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
FloatKNNAssigner(CentroidsProvider<float[]> provider, FloatFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.
IntKNNAssigner(CentroidsProvider<int[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
IntKNNAssigner(CentroidsProvider<int[]> provider, IntFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.
LongKNNAssigner(CentroidsProvider<long[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
LongKNNAssigner(CentroidsProvider<long[]> provider, LongFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.
ShortKNNAssigner(CentroidsProvider<short[]> provider, boolean exact, int numNeighbours) Construct the assigner using the given cluster data.
ShortKNNAssigner(CentroidsProvider<short[]> provider, ShortFVComparison comparison, int numNeighbours) Construct the assigner using the given cluster data and distance function.

Uses of CentroidsProvider in org.openimaj.ml.clustering.kmeans

Classes in org.openimaj.ml.clustering.kmeans that implement CentroidsProvider

Modifier and Type	Class and Description
static class	ByteKMeans.Result Result object for ByteKMeans, extending ByteCentroidsResult and ByteNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
static class	DoubleKMeans.Result Result object for DoubleKMeans, extending DoubleCentroidsResult and DoubleNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
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.
static class	FloatKMeans.Result Result object for FloatKMeans, extending FloatCentroidsResult and FloatNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
static class	IntKMeans.Result Result object for IntKMeans, extending IntCentroidsResult and IntNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
static class	LongKMeans.Result Result object for LongKMeans, extending LongCentroidsResult and LongNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
static class	ShortKMeans.Result Result object for ShortKMeans, extending ShortCentroidsResult and ShortNearestNeighboursProvider, as well as giving access to state information from the operation of the K-Means algorithm (i.e.
class	SphericalKMeansResult The result of a SpatialClusterer that just produces a flat set of centroids.

Uses of CentroidsProvider in org.openimaj.ml.clustering.rac

Classes in org.openimaj.ml.clustering.rac that implement CentroidsProvider

Modifier and Type	Class and Description
class	ClusterLimitedIntRAC Similar to IntRAC but explicitly specify the limit the number of clusters.
class	IntRAC An implementation of the RAC algorithm proposed by Ramanan and Niranjan.