class cv::BOWTrainer

Overview

Abstract base class for training the bag of visual words vocabulary from a set of descriptors. More…

#include <features2d.hpp>

class BOWTrainer
{
public:
    // methods

    void
    add(const Mat& descriptors);

    virtual
    void
    clear();

    virtual
    Mat
    cluster() const = 0;

    virtual
    Mat
    cluster(const Mat& descriptors) const = 0;

    int
    descriptorsCount() const;

    const std::vector<Mat>&
    getDescriptors() const;

protected:
    // fields

    std::vector<Mat> descriptors;
    int size;
};

// direct descendants

class BOWKMeansTrainer;

Detailed Documentation

Abstract base class for training the bag of visual words vocabulary from a set of descriptors.

For details, see, for example, Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :

Methods

void
add(const Mat& descriptors)

Adds descriptors to a training set.

The training set is clustered using clustermethod to construct the vocabulary.

Parameters:

descriptors

Descriptors to add to a training set. Each row of the descriptors matrix is a descriptor.

virtual
Mat
cluster() const = 0

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

virtual
Mat
cluster(const Mat& descriptors) const = 0

Clusters train descriptors.

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.

Parameters:

descriptors

Descriptors to cluster. Each row of the descriptors matrix is a descriptor. Descriptors are not added to the inner train descriptor set.

int
descriptorsCount() const

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

const std::vector<Mat>&
getDescriptors() const

Returns a training set of descriptors.