Class BOWTrainer

Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one.

public abstract class BOWTrainer : DisposableCvObject, IDisposable, ICvPtrHolder

Inherited Members: DisposableCvObject.ptr

DisposableCvObject.DisposeUnmanaged()

DisposableCvObject.CvPtr

DisposableObject.DataHandle

DisposableObject.IsDisposed

DisposableObject.IsEnabledDispose

DisposableObject.AllocatedMemory

DisposableObject.AllocatedMemorySize

DisposableObject.Dispose()

DisposableObject.Dispose(bool)

DisposableObject.DisposeManaged()

DisposableObject.AllocGCHandle(object)

DisposableObject.AllocMemory(int)

DisposableObject.NotifyMemoryPressure(long)

DisposableObject.ThrowIfDisposed()

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Methods

Adds descriptors to a training set.

public void Add(Mat descriptors)

descriptors Mat: descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor. The training set is clustered using clustermethod to construct the vocabulary.

public virtual void Clear()

Clusters train descriptors.

public abstract Mat Cluster()

Clusters train descriptors.

public abstract Mat Cluster(Mat descriptors)

descriptors Mat: Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered.In the second variant, input descriptors are clustered.

Returns the count of all descriptors stored in the training set.

public int DescriptorsCount()

Returns a training set of descriptors.

public Mat[] GetDescriptors()