https://github.com/dorian3d/DBoW2

Enhanced hierarchical bag-of-word library for C++
https://github.com/dorian3d/DBoW2

Last synced: 18 days ago
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Enhanced hierarchical bag-of-word library for C++

• Host: GitHub
• URL: https://github.com/dorian3d/DBoW2
• Owner: dorian3d
• License: other
• Created: 2014-10-26T19:40:38.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2021-11-24T11:28:16.000Z (almost 3 years ago)
• Last Synced: 2024-07-31T22:44:42.099Z (3 months ago)
• Language: C++
• Size: 693 KB
• Stars: 850
• Watchers: 35
• Forks: 365
• Open Issues: 30
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
DBoW2

=====

DBoW2 is an improved version of the DBow library, an open source C++ library for indexing and converting images into a bag-of-word representation. It implements a hierarchical tree for approximating nearest neighbours in the image feature space and creating a visual vocabulary. DBoW2 also implements an image database with inverted and direct files to index images and enabling quick queries and feature comparisons. The main differences with the previous DBow library are:

  * DBoW2 classes are templated, so it can work with any type of descriptor.

  * DBoW2 is shipped with classes to directly work with ORB or BRIEF descriptors.

  * DBoW2 adds a direct file to the image database to do fast feature comparison. This is used by DLoopDetector.

  * DBoW2 does not use a binary format any longer. On the other hand, it uses the OpenCV storage system to save vocabularies and databases. This means that these files can be stored as plain text in YAML format, making compatibility easier, or compressed in gunzip format (.gz) to reduce disk usage.

  * Some pieces of code have been rewritten to optimize speed. The interface of DBoW2 has been simplified.

  * For performance reasons, DBoW2 does not support stop words.

DBoW2 requires OpenCV and the `Boost::dynamic_bitset` class in order to use the BRIEF version.

DBoW2, along with DLoopDetector, has been tested on several real datasets, yielding an execution time of 3 ms to convert the BRIEF features of an image into a bag-of-words vector and 5 ms to look for image matches in a database with more than 19000 images.

## Citing

If you use this software in an academic work, please cite:

    @ARTICLE{GalvezTRO12,

      author={G\'alvez-L\'opez, Dorian and Tard\'os, J. D.},

      journal={IEEE Transactions on Robotics},

      title={Bags of Binary Words for Fast Place Recognition in Image Sequences},

      year={2012},

      month={October},

      volume={28},

      number={5},

      pages={1188--1197},

      doi={10.1109/TRO.2012.2197158},

      ISSN={1552-3098}

    }

}

## Usage notes

### Weighting and Scoring

DBoW2 implements the same weighting and scoring mechanisms as DBow. Check them here. The only difference is that DBoW2 scales all the scores to [0..1], so that the scaling flag is not used any longer.

### Save & Load

All vocabularies and databases can be saved to and load from disk with the save and load member functions. When a database is saved, the vocabulary it is associated with is also embedded in the file, so that vocabulary and database files are completely independent.

You can also add the vocabulary or database data to any file opened with a `cv::FileStorage` structure.

You can save the vocabulary or the database with any file extension. If you use .gz, the file is automatically compressed (OpenCV behaviour).

## Implementation notes

### Template parameters

DBoW2 has two main classes: `TemplatedVocabulary` and `TemplatedDatabase`. These implement the visual vocabulary to convert images into bag-of-words vectors and the database to index images. These classes are templated:

    template

    class TemplatedVocabulary

    {

      ...

    };

    template

    class TemplatedDatabase

    {

      ...

    };

Two classes must be provided: `TDescriptor` is the data type of a single descriptor vector, and `F`, a class with the functions to manipulate descriptors, derived from `FClass`.

For example, to work with ORB descriptors, `TDescriptor` is defined as `cv::Mat` (of type `CV_8UC1`), which is a single row that contains 32 8-bit values. When features are extracted from an image, a `std::vector` must be obtained. In the case of BRIEF, `TDescriptor` is defined as `boost::dynamic_bitset<>`.

The `F` parameter is the name of a class that implements the functions defined in `FClass`. These functions get `TDescriptor` data and compute some result. Classes to deal with ORB and BRIEF descriptors are already included in DBoW2. (`FORB`, `FBrief`).

### Predefined Vocabularies and Databases

To make it easier to use, DBoW2 defines two kinds of vocabularies and databases: `OrbVocabulary`, `OrbDatabase`, `BriefVocabulary`, `BriefDatabase`. Please, check the demo application to see how they are created and used.