Common Interfaces of Descriptor Extractors

Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch between different algorithms solving the same problem. This section is devoted to computing descriptors represented as vectors in a multidimensional space. All objects that implement the vector descriptor extractors inherit the DescriptorExtractor interface.

DescriptorExtractor

class DescriptorExtractor : public Algorithm

Abstract base class for computing descriptors for image keypoints.

class CV_EXPORTS DescriptorExtractor
{
public:
    virtual ~DescriptorExtractor();

    void compute( const Mat& image, vector<KeyPoint>& keypoints,
                  Mat& descriptors ) const;
    void compute( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints,
                  vector<Mat>& descriptors ) const;

    virtual void read( const FileNode& );
    virtual void write( FileStorage& ) const;

    virtual int descriptorSize() const = 0;
    virtual int descriptorType() const = 0;

    static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );

protected:
    ...
};

In this interface, a keypoint descriptor can be represented as a dense, fixed-dimension vector of a basic type. Most descriptors follow this pattern as it simplifies computing distances between descriptors. Therefore, a collection of descriptors is represented as Mat , where each row is a keypoint descriptor.

DescriptorExtractor::compute

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

C++: void DescriptorExtractor::compute(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const

C++: void DescriptorExtractor::compute(const vector<Mat>& images, vector<vector<KeyPoint>>& keypoints, vector<Mat>& descriptors) const

Parameters:

• image – Image.
• images – Image set.
• keypoints – Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
• descriptors – Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]`. Row ``j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

DescriptorExtractor::create

Creates a descriptor extractor by name.

C++: Ptr<DescriptorExtractor> DescriptorExtractor::create(const string& descriptorExtractorType)

Parameters:

• descriptorExtractorType – Descriptor extractor type.

The current implementation supports the following types of a descriptor extractor:

• "SIFT" – SIFT
• "SURF" – SURF
• "ORB" – ORB
• "BRIEF" – BriefDescriptorExtractor

A combined format is also supported: descriptor extractor adapter name ( "Opponent" – OpponentColorDescriptorExtractor ) + descriptor extractor name (see above), for example: "OpponentSIFT" .

OpponentColorDescriptorExtractor

class OpponentColorDescriptorExtractor : public DescriptorExtractor

Class adapting a descriptor extractor to compute descriptors in the Opponent Color Space (refer to Van de Sande et al., CGIV 2008 Color Descriptors for Object Category Recognition). Input RGB image is transformed in the Opponent Color Space. Then, an unadapted descriptor extractor (set in the constructor) computes descriptors on each of three channels and concatenates them into a single color descriptor.

class OpponentColorDescriptorExtractor : public DescriptorExtractor
{
public:
    OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor );

    virtual void read( const FileNode& );
    virtual void write( FileStorage& ) const;
    virtual int descriptorSize() const;
    virtual int descriptorType() const;
protected:
    ...
};

BriefDescriptorExtractor

class BriefDescriptorExtractor : public DescriptorExtractor

Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V., Strecha C., Fua P. BRIEF: Binary Robust Independent Elementary Features , 11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer, September 2010.

class BriefDescriptorExtractor : public DescriptorExtractor
{
public:
    static const int PATCH_SIZE = 48;
    static const int KERNEL_SIZE = 9;

    // bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.
    BriefDescriptorExtractor( int bytes = 32 );

    virtual void read( const FileNode& );
    virtual void write( FileStorage& ) const;
    virtual int descriptorSize() const;
    virtual int descriptorType() const;
protected:
    ...
};