快速近似最近邻搜索库 FLANN - Fast Library for Approximate Nearest Neighbors
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 choosing 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
- (14 December 2012) Version 1.8.0 is out bringing incremental addition/reamoval of points to/from indexes
- (20 December 2011) Version 1.7.0 is out bringing two new index types and several other improvements.
- You can find binary installers for FLANN on 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 way to use custom distances, kd-tree implementation optimized for low dimensionality search and experimental MPI support
- New release introducing new C++ templated API, thread-safe search, save/load of indexes and more.
- The FLANN license was changed from LGPL to BSD.
How fast is it?
In our experiments we have found FLANN to be about one order of magnitude faster on many datasets (in query time), than previously available approximate nearest neighbor search software.
Publications
More information and experimental results can be 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", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009 [PDF] [BibTeX]
Getting FLANN
The latest version of FLANN can be downloaded from here:
- Version 1.8.4 (15 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 (14 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 has changed and it is not possible to load indexes saved with an 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, then it's recommended that you checkout the git source repository: git clone git://github.com/mariusmuja/flann.git
If you just want to browse the repository, you can do so by going here.
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 have any questions or comments please email them to: mariusm@cs.ubc.ca.
Please report bugs or feature requests using github's issue tracker.
from: http://www.cs.ubc.ca/research/flann/
快速近似最近邻搜索库 FLANN - Fast Library for Approximate Nearest Neighbors的更多相关文章
- Approximate Nearest Neighbors.接近最近邻搜索
(一):次优最近邻:http://en.wikipedia.org/wiki/Nearest_neighbor_search 有少量修改:如有疑问,请看链接原文.....1.Survey:Neares ...
- facebook 相似性搜索库 faiss
faiss 个人理解: https://github.com/facebookresearch/faiss 上把代码clone下来,make编译 我们将CNN中经过若干个卷积/激励/池化层后得到的激活 ...
- 近似最近邻算法-annoy解析
转自https://www.cnblogs.com/futurehau/p/6524396.html Annoy是高维空间求近似最近邻的一个开源库. Annoy构建一棵二叉树,查询时间为O(logn) ...
- 如何快速构建React组件库
前言 俗话说:"麻雀虽小,五脏俱全",搭建一个组件库,知之非难,行之不易,涉及到的技术方方面面,犹如海面风平浪静,实则暗礁险滩,处处惊险- 目前团队内已经有较为成熟的 Vue 技术 ...
- [转帖]运行时库(runtime library)
运行时库(runtime library) https://blog.csdn.net/xitie8523/article/details/82712105 没学过这些东西 或者当时上课没听 又或者 ...
- 代码的坏味道(22)——不完美的库类(Incomplete Library Class)
坏味道--不完美的库类(Incomplete Library Class) 特征 当一个类库已经不能满足实际需要时,你就不得不改变这个库(如果这个库是只读的,那就没辙了). 问题原因 许多编程技术都建 ...
- Glibc辅助运行库 (C RunTime Library): crt0.o,crt1.o,crti.o crtn.o,crtbegin.o crtend.o
crt1.o, crti.o, crtbegin.o, crtend.o, crtn.o 等目标文件和daemon.o(由我们自己的C程序文件产生)链接成一个执行文件.前面这5个目标文件的作用分别是启 ...
- python_如何快速下载安装第三方库?
如何快速下载安装第三方库? --通过 淘宝源 https://mirrors.aliyun.com/pypi/simple/ 本国网络进行快速安装 如何执行安装命令? pip install Dja ...
- sklearn:最近邻搜索sklearn.neighbors
http://blog.csdn.net/pipisorry/article/details/53156836 ball tree k-d tree也有问题[最近邻查找算法kd-tree].矩形并不是 ...
随机推荐
- 【Java】 参数的传递:值传递与引用传递讨论
内容稍多,可直接看第4点的讨论结果 前言 在涉及到传递参数给方法时,容易出现一些参数传递错误的问题,这就涉及到了参数的传递问题,必须搞清楚:参数是如何传递到方法中的?一般来说,参数的传递可以分为两种: ...
- python抓包模块
pcapy模块 安装 yum install -y epel-release yum install -y pip gcc gcc-c++ libpcap-devel python-deve ...
- POJ1222 EXTENDED LIGHTS OUT 高斯消元 XOR方程组
http://poj.org/problem?id=1222 在学校oj用搜索写了一次,这次写高斯消元,haoi现场裸xor方程消元没写出来,真实zz. #include<iostream> ...
- Codedforces 1076G Array Game 线段树
题意 现在cf上看题意真nm麻烦,有道网页翻译和谷歌翻译鬼畜的一匹 两个人在玩一个游戏. 有一个有\(n\)个数序列\(B\),一开始有一个棋子在\(B\)的第一个位置. 双方轮流操作,第一次操作前将 ...
- HTML5区块和大纲算法
原文链接: Using HTML sections and outlines - Mozilla Developer Network 每集HTML5+CSS3网页布局教程-2大纲算法 HTML5标准带 ...
- bzoj 1658: [Usaco2006 Mar]Water Slides 滑水
题解: 很神奇的做法,把点分成入度大于出度和入度小于出度两种. 然后入度大于出度的点必须走到某个点,所以排序贪心. #include<stdio.h> #include<iostre ...
- 【BZOJ】4985: 评分【DP】
4985: 评分 Time Limit: 10 Sec Memory Limit: 64 MBSubmit: 148 Solved: 75[Submit][Status][Discuss] Des ...
- BZOJ5217: [Lydsy2017省队十连测]航海舰队 FFT
被FFT的空间卡了半天 后来发现根本不用开那么大... 首先可以把包含舰艇的那个小矩形找出来 将它一行一行连接成一个串T 其中舰艇位置为1其他位置为0 将大矩形也连成串S 其中礁石为1其他为0 两个串 ...
- Codeforces Round #256 (Div. 2) E Divisors
E. Divisors Bizon the Champion isn't just friendly, he also is a rigorous coder. Let's define functi ...
- Codeforces Round #296 (Div. 1) A. Glass Carving Set的妙用
A. Glass Carving time limit per test 2 seconds memory limit per test 256 megabytes input standard in ...