OBJECT
- Type of object being annotatedANNOTATION
- Type of annotation@Reference(type=Inproceedings, author={"Jonathan Hare","Paul Lewis"}, title="Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces", year="2010", booktitle="Imaging and Printing in a Web 2.0 World; and Multimedia Content Access: Algorithms and Systems IV", url="http://eprints.soton.ac.uk/268496/", note=" Event Dates: 17-21 Jan 2010", month="January", publisher="SPIE", volume="7540") public class DenseLinearTransformAnnotator<OBJECT,ANNOTATION> extends BatchAnnotator<OBJECT,ANNOTATION>
Modifier and Type | Field and Description |
---|---|
protected int |
k |
protected List<ANNOTATION> |
terms |
protected Jama.Matrix |
transform |
Constructor and Description |
---|
DenseLinearTransformAnnotator(int k,
FeatureExtractor<? extends FeatureVector,OBJECT> extractor)
Construct with the given number of dimensions and feature extractor.
|
Modifier and Type | Method and Description |
---|---|
List<ScoredAnnotation<ANNOTATION>> |
annotate(OBJECT image)
Generate annotations for the given object.
|
Set<ANNOTATION> |
getAnnotations() |
void |
train(List<? extends Annotated<OBJECT,ANNOTATION>> data)
Train the object with the given data.
|
train
classify
protected List<ANNOTATION> terms
protected Jama.Matrix transform
protected int k
public DenseLinearTransformAnnotator(int k, FeatureExtractor<? extends FeatureVector,OBJECT> extractor)
k
- the number of dimensions (rank of the pseudo-inverse)extractor
- the feature extractorpublic void train(List<? extends Annotated<OBJECT,ANNOTATION>> data)
BatchTrainer
BatchTrainer
s should reset the underlying
model everytime this method is called.data
- the training datapublic List<ScoredAnnotation<ANNOTATION>> annotate(OBJECT image)
Annotator
image
- the imagepublic Set<ANNOTATION> getAnnotations()