object_categorization.rst



Object Categorization
This section describes approaches based on local 2D features and used to categorize objects.

Note

A complete Bag-Of-Words sample can be found at opencv_source_code/samples/cpp/bagofwords_classification.cpp
(Python) An example using the features2D framework to perform object categorization can be found at opencv_source_code/samples/python2/find_obj.py


BOWTrainer
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.
class BOWTrainer
{
public:
    BOWTrainer(){}
    virtual ~BOWTrainer(){}

    void add( const Mat& descriptors );
    const vector<Mat>& getDescriptors() const;
    int descriptorsCount() const;

    virtual void clear();

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

protected:
    ...
};


BOWTrainer::add
Adds descriptors to a training set.
The training set is clustered using clustermethod to construct the vocabulary.

BOWTrainer::getDescriptors
Returns a training set of descriptors.

BOWTrainer::descriptorsCount
Returns the count of all descriptors stored in the training set.

BOWTrainer::cluster
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.

BOWKMeansTrainer
:ocv:func:`kmeans` -based class to train visual vocabulary using the bag of visual words approach.
class BOWKMeansTrainer : public BOWTrainer
{
public:
    BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
                      int attempts=3, int flags=KMEANS_PP_CENTERS );
    virtual ~BOWKMeansTrainer(){}

    // Returns trained vocabulary (i.e. cluster centers).
    virtual Mat cluster() const;
    virtual Mat cluster( const Mat& descriptors ) const;

protected:
    ...
};


BOWKMeansTrainer::BOWKMeansTrainer
The constructor.

BOWImgDescriptorExtractor
Class to compute an image descriptor using the bag of visual words. Such a computation consists of the following steps:


Compute descriptors for a given image and its keypoints set.
Find the nearest visual words from the vocabulary for each keypoint descriptor.
Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the vocabulary in the given image.


The class declaration is the following:
class BOWImgDescriptorExtractor
{
public:
    BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
                               const Ptr<DescriptorMatcher>& dmatcher );
    BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
    virtual ~BOWImgDescriptorExtractor(){}

    void setVocabulary( const Mat& vocabulary );
    const Mat& getVocabulary() const;
    void compute( InputArray image, vector<KeyPoint>& keypoints,
                  OutputArray imgDescriptor,
                  vector<vector<int> >* pointIdxsOfClusters=0,
                  Mat* descriptors=0 );
    void compute( InputArray descriptors, OutputArray imgDescriptor,
                  std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
    int descriptorSize() const;
    int descriptorType() const;

protected:
    ...
};


BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
The constructor.

BOWImgDescriptorExtractor::setVocabulary
Sets a visual vocabulary.

BOWImgDescriptorExtractor::getVocabulary
Returns the set vocabulary.

BOWImgDescriptorExtractor::compute
Computes an image descriptor using the set visual vocabulary.

BOWImgDescriptorExtractor::descriptorSize
Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.

BOWImgDescriptorExtractor::descriptorType
Returns an image descriptor type.