OpenCV  4.5.2-dev
Open Source Computer Vision
Public Types | Static Public Member Functions | List of all members
cv::xfeatures2d::BEBLID Class Reference

Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [232] . More...

#include <opencv2/xfeatures2d.hpp>

Inheritance diagram for cv::xfeatures2d::BEBLID:
cv::Feature2D cv::Algorithm

Public Types

enum  BeblidSize {
  SIZE_512_BITS = 100,
  SIZE_256_BITS = 101
}
 Descriptor number of bits, each bit is a boosting weak-learner. The user can choose between 512 or 256 bits. More...
 

Static Public Member Functions

static Ptr< BEBLIDcreate (float scale_factor, int n_bits=BEBLID::SIZE_512_BITS)
 Creates the BEBLID descriptor. More...
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > load (const String &filename, const String &objname=String())
 Loads algorithm from the file. More...
 
template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String. More...
 
template<typename _Tp >
static Ptr< _Tp > read (const FileNode &fn)
 Reads algorithm from the file node. More...
 

Additional Inherited Members

- Public Member Functions inherited from cv::Feature2D
virtual ~Feature2D ()
 
virtual void compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors)
 Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). More...
 
virtual void compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)
 
virtual int defaultNorm () const
 
virtual int descriptorSize () const
 
virtual int descriptorType () const
 
virtual void detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
 Detects keypoints in an image (first variant) or image set (second variant). More...
 
virtual void detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())
 
virtual void detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
 
virtual bool empty () const CV_OVERRIDE
 Return true if detector object is empty. More...
 
virtual String getDefaultName () const CV_OVERRIDE
 
void read (const String &fileName)
 
virtual void read (const FileNode &) CV_OVERRIDE
 Reads algorithm parameters from a file storage. More...
 
void write (const String &fileName) const
 
virtual void write (FileStorage &) const CV_OVERRIDE
 Stores algorithm parameters in a file storage. More...
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state. More...
 
virtual void save (const String &filename) const
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 
- Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const
 

Detailed Description

Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [232] .

BEBLID [232] is a efficient binary descriptor learned with boosting. It is able to describe keypoints from any detector just by changing the scale_factor parameter. In several benchmarks it has proved to largely improve other binary descriptors like ORB or BRISK with the same efficiency. BEBLID describes using the difference of mean gray values in different regions of the image around the KeyPoint, the descriptor is specifically optimized for image matching and patch retrieval addressing the asymmetries of these problems.

If you find this code useful, please add a reference to the following paper:

Iago Suárez, Ghesn Sfeir, José M. Buenaposada, and Luis Baumela. BEBLID: Boosted efficient binary local image descriptor. Pattern Recognition Letters, 133:366–372, 2020.

The descriptor was trained using 1 million of randomly sampled pairs of patches (20% positives and 80% negatives) from the Liberty split of the UBC datasets [271] as described in the paper [232]. You can check in the AKAZE example how well BEBLID works. Detecting 10000 keypoints with ORB and describing with BEBLID obtains 561 inliers (75%) whereas describing with ORB obtains only 493 inliers (63%).

Member Enumeration Documentation

◆ BeblidSize

Descriptor number of bits, each bit is a boosting weak-learner. The user can choose between 512 or 256 bits.

Enumerator
SIZE_512_BITS 
SIZE_256_BITS 

Member Function Documentation

◆ create()

static Ptr<BEBLID> cv::xfeatures2d::BEBLID::create ( float  scale_factor,
int  n_bits = BEBLID::SIZE_512_BITS 
)
static
Python:
cv.xfeatures2d.BEBLID_create(scale_factor[, n_bits]) -> retval

Creates the BEBLID descriptor.

Parameters
scale_factorAdjust the sampling window around detected keypoints:
  • 1.00f should be the scale for ORB keypoints
  • 6.75f should be the scale for SIFT detected keypoints
  • 6.25f is default and fits for KAZE, SURF detected keypoints
  • 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
n_bitsDetermine the number of bits in the descriptor. Should be either BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS.

The documentation for this class was generated from the following file: