Fast approximate nearest Neighbor Search Library Flann-fast library for approximate Nearest neighbors

Source: Internet
Author: User

What is FLANN?

FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically ch Oosing the best algorithm and optimum parameters depending on the dataset.

FLANN is written in C + + and contains bindings for the following languages:c, MATLAB and Python.

News
    • (December 2012) Version 1.8.0 is out bringing incremental addition/reamoval of points to/from indexes
    • (2011 December) Version 1.7.0 is out bringing the new index types and several other improvements.
    • You can find binary installers for FLANN in the Point Cloud Library project page. Thanks to the PCL developers!
    • Mac OS X Users can install Flann though MacPorts (thanks to Mark Moll for maintaining the Portfile)
    • NEW release introducing an easier-on-use custom distances, kd-tree implementation optimized for low dimensionality sea RCH and experimental MPI support
    • New Release introducing new C + + templated API, Thread-safe Search, save/load of indexes and more.
    • The FLANN license is changed from LGPL to BSD.
How fast is it?

In our experiments we had found FLANN to is about one order of magnitude faster on many datasets (in query time), than PR eviously available Approximate nearest neighbor search software.

Publications

More information and experimental results can is found in the following papers:

    • Marius Muja and David G. Lowe: "Scalable Nearest Neighbor algorithms for high dimensional Data". Pattern Analysis and Machine Intelligence (Pami), vol. 36, 2014. [PDF] [BibTeX]
    • Marius Muja and David G. Lowe: "Fast Matching of Binary Features". Conference on computer and Robot Vision (CRV) 2012. [PDF] [BibTeX]
    • Marius Muja and David G. Lowe, "Fast approximate Nearest neighbors with Automatic algorithm Configuration", internation Al Conference on computer Vision theory and Applications (Visapp '), [PDF] [BibTeX]

Getting FLANN

The latest version of FLANN can is downloaded from here:

    • Version 1.8.4 (January 2013)
      Changes from 1.8.3:
      • Fixed Memory Leak and OpenMP compilation under MSVC
      Flann-1.8.4-src.zip (Source code)
      User Manual
      Changelog

    • Version 1.8.0 (December 2012)
      Changes:
      • incremental addition and removal of points to/from indexes
      • More flexible index serialization
      • Replaced TBB multi-threading support with OpenMP
      • Bug fixes
      • Note:due to changes in the library, the On-disk format of the saved indexes have changed and it is not possible to load in Dexes saved with a older version of the library.

If you don ' t want to compile FLANN from source you can try the binary installers prepared by the point Cloud Library (PCL) Project here (Ubuntu/debian PPA, Windows installers and Mac OS X Universal Binary).

If you want to try out the latest changes or contribute to FLANN and then it's recommended that you checkout the Git source R Epository:git clone git://github.com/mariusmuja/flann.git

If you just want-to-browse the repository, you can do so by going.

System Requirements

The FLANN library was developed and tested under Linux. A C + + compiler is required to build FLANN. The Python bindings require the presence of the numerical Python (numpy) package.

Conditions of Use

FLANN is distributed under the terms of the BSD License.

Questions/comments

If you are questions or comments please email them to: [email protected].

Please report bugs or feature requests using GitHub ' s issue tracker.

from:http://www.cs.ubc.ca/research/flann/

Fast approximate nearest Neighbor Search Library Flann-fast library for approximate Nearest neighbors

Related Article

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.