BagOfVisualWordsTPoint, TFeature, TClustering, TExtractor Class

[SerializableAttribute]
public class BagOfVisualWords<TPoint, TFeature, TClustering, TExtractor> : BaseBagOfVisualWords<BagOfVisualWords<TPoint, TFeature, TClustering, TExtractor>, TPoint, TFeature, TClustering, TExtractor>
where TPoint : Object, IFeatureDescriptor<TFeature>
where TClustering : Object, IUnsupervisedLearning<IClassifier<TFeature, int>, TFeature, int>
where TExtractor : Object, IImageFeatureExtractor<TPoint>

<SerializableAttribute>
Public Class BagOfVisualWords(Of TPoint As {Object, IFeatureDescriptor(Of TFeature)}, TFeature, TClustering As {Object, IUnsupervisedLearning(Of IClassifier(Of TFeature, Integer), TFeature, Integer)}, TExtractor As {Object, IImageFeatureExtractor(Of TPoint)})
	Inherits BaseBagOfVisualWords(Of BagOfVisualWords(Of TPoint, TFeature, TClustering, TExtractor), TPoint, TFeature, TClustering, TExtractor)

Type Parameters

TPoint

The IFeaturePointT type to be used with this class, such as SpeededUpRobustFeaturePoint.

TFeature

The feature type of the TPoint, such as double.

TClustering

The type of the clustering algorithm to be used to cluster the visual features and form visual codewords.

TExtractor

The type of the feature detector used to extract features from the images.

The bag-of-words (BoW) model can be used to extract finite length features from otherwise varying length representations. This class can uses any feature detector to determine a coded representation for a given image.

For a simpler, non-generic version of the Bag-of-Words model which defaults to the SURF features detector, please see BagOfVisualWords.

Please see BagOfVisualWords.

Reference