manchas2k4/kdtree-cpp

Kd-Tree library for kNN and range queries in plain C++98

C++ Kd-Tree Library

This is a standalone C++ version of the kd-tree implementation in the Gamera framework that has been extended by a range search and has been relicensed by the original author under a BSD style license. It is written in plain C++98 and does not depend on any third party libraries.

A kd-tree is a data structure that allows for nearest neighbor queries in expected O(log(n)) time. The creation time of a kd-tree is O(n log(n)). This library offers four additional features not commonly found in kd-tree implementations:

• kNN search with optional additional search condition (search predicate)
• support for Euclidean, Manhatten, and Maximum distance and their weighted extensions (Euclidean is the default distance)
• range query
• nodes can store a pointer to arbitrary data as void*

Details about the design and usage of the library can be found in

C. Dalitz: Kd-Trees for Document Layout Analysis. In C. Dalitz (Ed.): "Document Image Analysis with the Gamera Framework." Schriftenreihe des Fachbereichs Elektrotechnik und Informatik, Hochschule Niederrhein, vol. 8, pp. 39-52, Shaker Verlag (2009)

Please cite this article when using this library in a scientific project.

Usage of the library

The library consists of two files:

• kdtree.hpp contains the class declarations
• kdtree.cpp contains the implementation and must be compiled

The sample program demo_kdtree.cpp shows how the following operations are done:

• creation of a kd-tree
• k nearest neighbor search
• k nearest neighbor search with additional search predicate
• range query

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Note: The present implementation does not use the C++ template mechanism for storing data points of only a specific dimension defined at compile time. The dimension is instead determined at run time and the data points are stored as nested vectors. This can slow down memory management considerably when compiling in debug mode. It is thus advisable, to turn off debug flags when compiling.

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Authors & Copyright

• Christoph Dalitz, 2018-2022, https://lionel.kr.hsnr.de/~dalitz/data/kdtree/
• Jens Wilberg, 2018

For licensing information, see the file LICENSE for details.