public class FactoryAssociation
extends java.lang.Object
TupleDesc_F64 features.| Constructor and Description |
|---|
FactoryAssociation() |
| Modifier and Type | Method and Description |
|---|---|
static <D> ScoreAssociation<D> |
defaultScore(java.lang.Class<D> tupleType)
Given a feature descriptor type it returns a "reasonable" default
ScoreAssociation. |
static <D> AssociateDescription<D> |
greedy(ScoreAssociation<D> score,
double maxError,
boolean backwardsValidation)
Returns an algorithm for associating features together which uses a brute force greedy algorithm.
|
static AssociateDescription<TupleDesc_F64> |
kdRandomForest(int dimension,
int maxNodesSearched,
int numTrees,
int numConsiderSplit,
long randomSeed)
Approximate association using multiple random K-D trees (random forest) for descriptors with a high degree of
freedom, e.g.
|
static AssociateDescription<TupleDesc_F64> |
kdtree(int dimension,
int maxNodesSearched)
Approximate association using a K-D tree degree of moderate size (10-15) that uses a best-bin-first search
order.
|
static <D> ScoreAssociation<D> |
scoreEuclidean(java.lang.Class<D> tupleType,
boolean squared)
Scores features based on the Euclidean distance between them.
|
static <D> ScoreAssociation<D> |
scoreHamming(java.lang.Class<D> tupleType)
Hamming distance between two binary descriptors.
|
static ScoreAssociation<NccFeature> |
scoreNcc()
Scores features based on their Normalized Cross-Correlation (NCC).
|
static <D> ScoreAssociation<D> |
scoreSad(java.lang.Class<D> tupleType)
Scores features based on Sum of Absolute Difference (SAD).
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public static <D> AssociateDescription<D> greedy(ScoreAssociation<D> score, double maxError, boolean backwardsValidation)
AssociateGreedy for details.D - Data structure being associatedscore - Computes the fit score between two features.maxError - Maximum allowed error/fit score between two features. To disable set to Double.MAX_VALUEbackwardsValidation - If true associations are validated by associating in the reverse direction. If the
forward and reverse matches fit an association is excepted.public static AssociateDescription<TupleDesc_F64> kdtree(int dimension, int maxNodesSearched)
dimension - Number of elements in the feature vectormaxNodesSearched - Maximum number of nodes it will search. Controls speed and accuracy.AssociateNearestNeighbor,
KdTreeSearch1Bbfpublic static AssociateDescription<TupleDesc_F64> kdRandomForest(int dimension, int maxNodesSearched, int numTrees, int numConsiderSplit, long randomSeed)
dimension - Number of elements in the feature vectormaxNodesSearched - Maximum number of nodes it will search. Controls speed and accuracy.numTrees - Number of trees that are considered. Try 10 and tune.numConsiderSplit - Number of nodes that are considered when generating a tree. Must be less than the
point's dimension. Try 5randomSeed - Seed used by random number generatorAssociateNearestNeighbor,
KdForestBbfSearchpublic static <D> ScoreAssociation<D> defaultScore(java.lang.Class<D> tupleType)
ScoreAssociation.tupleType - Class type which extends TupleDescpublic static <D> ScoreAssociation<D> scoreSad(java.lang.Class<D> tupleType)
tupleType - Type of descriptor being scoredpublic static ScoreAssociation<NccFeature> scoreNcc()
public static <D> ScoreAssociation<D> scoreEuclidean(java.lang.Class<D> tupleType, boolean squared)
tupleType - Type of descriptor being scoredsquared - IF true the distance squared is returned. Usually truepublic static <D> ScoreAssociation<D> scoreHamming(java.lang.Class<D> tupleType)
tupleType - Type of descriptor being scored