Object Categorization

Some approaches based on local 2D features and used to object categorization are described in this section.

BOWTrainer

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BOWTrainer

Abstract base class for training ‘’bag of visual words’’ vocabulary from a set of descriptors. See e.g. ‘’Visual Categorization with Bags of Keypoints’’ of 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 descripotorsCount() const;

    virtual void clear();

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

protected:
    ...
};

cv::BOWTrainer::add

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void BOWTrainer::add(const Mat& descriptors)

Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.

Parameters:

• descriptors – Descriptors to add to training set. Each row of descriptors matrix is a one descriptor.

cv::BOWTrainer::getDescriptors

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

Returns training set of descriptors.

cv::BOWTrainer::descripotorsCount

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const vector<Mat>& BOWTrainer::descripotorsCount() const

Returns count of all descriptors stored in the training set.

cv::BOWTrainer::cluster

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Mat BOWTrainer::cluster() const

Cluster train descriptors. Vocabulary consists from cluster centers. So this method

returns vocabulary. In first method variant the stored in object train descriptors will be clustered, in second variant – input descriptors will be clustered.

Mat BOWTrainer::cluster(const Mat& descriptors) const

Parameters:

• descriptors – Descriptors to cluster. Each row of descriptors matrix is a one descriptor. Descriptors will not be added to the inner train descriptor set.

BOWKMeansTrainer

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BOWKMeansTrainer

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:
    ...
};

To gain an understanding of constructor parameters see kmeans() function arguments.

BOWImgDescriptorExtractor

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BOWImgDescriptorExtractor

Class to compute image descriptor using ‘’bad of visual words’‘. In few,

such computing consists from the following steps: 1. Compute descriptors for given image and it’s keypoints set,

2. Find nearest visual words from vocabulary for each keypoint descriptor, 3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e.

i -bin of the histogram is a frequency of i -word of vocabulary in the given image.

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

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

protected:
    ...
};

cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor

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BOWImgDescriptorExtractor::BOWImgDescriptorExtractor(const Ptr<DescriptorExtractor>& dextractor, const Ptr<DescriptorMatcher>& dmatcher)

Constructor.

Parameters:

• dextractor – Descriptor extractor that will be used to compute descriptors for input image and it’s keypoints.
• dmatcher – Descriptor matcher that will be used to find nearest word of trained vocabulary to each keupoints descriptor of the image.

cv::BOWImgDescriptorExtractor::setVocabulary

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void BOWImgDescriptorExtractor::setVocabulary(const Mat& vocabulary)

Method to set visual vocabulary.

Parameters:

• vocabulary – Vocabulary (can be trained using inheritor of BOWTrainer() ). Each row of vocabulary is a one visual word (cluster center).

cv::BOWImgDescriptorExtractor::getVocabulary

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const Mat& BOWImgDescriptorExtractor::getVocabulary() const

Returns set vocabulary.

cv::BOWImgDescriptorExtractor::compute

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void BOWImgDescriptorExtractor::compute(const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0)

Compute image descriptor using set visual vocabulary.

Parameters:

• image – The image. Image descriptor will be computed for this.
• keypoints – Keypoints detected in the input image.
• imgDescriptor – This is output, i.e. computed image descriptor.
• pointIdxsOfClusters –

  Indices of keypoints which belong to the cluster, i.e.

  pointIdxsOfClusters[i] is keypoint indices which belong

  to the i- cluster (word of vocabulary) (returned if it is not 0.)

• descriptors – Descriptors of the image keypoints (returned if it is not 0.)

cv::BOWImgDescriptorExtractor::descriptorSize

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int BOWImgDescriptorExtractor::descriptorSize() const

Returns image discriptor size, if vocabulary was set, and 0 otherwise.

cv::BOWImgDescriptorExtractor::descriptorType

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int BOWImgDescriptorExtractor::descriptorType() const

Returns image descriptor type.