Update: This implementation is also part of davidstutz/superpixel-benchmark.
This repository contains an implementation of the graph-based image segmentation algorithms described in [1] focussing on generating oversegmentations, also referred to as superpixels.
[1] P. F. Felzenswalb and D. P. Huttenlocher.
Efficient Graph-Based Image Segmentation.
International Journal of Computer Vision, volume 59, number 2, 2004.
The implementation was used in [2] for evaluation.
[2] D. Stutz, A. Hermans, B. Leibe.
Superpixels: An Evaluation of the State-of-the-Art.
Computer Vision and Image Understanding, 2018.
The implementation is based on CMake, OpenCV and Boost. The following steps have been tested on Ubuntu 12.04:
$ sudo apt-get install build-essential
$ sudo apt-get install cmake
$ sudo apt-get install libboost-all-dev
OpenCV can either be installed following these instructions, or using:
$ sudo apt-get install libopencv-dev
With all requirements installed, run:
$ mkdir build
$ cd build
$ cmake ..
$ make
The provided tool can easily be used as follows (from within the build directory):
# Show a help message.
$ ../bin/refh_cli --help
Allowed options:
-h [ --help ] produce help message
--input arg folder containing the images to process
--threshold arg (=20) constant for threshold function
--minimum-size arg (=10) minimum component size
--output arg (=output) save segmentation as CSV file and contour images
# Oversegment the provided examples:
$ ../bin/refh_cli ../data/ ../output --threshold 255
The latter command will create the output directory containing the oversegmentations as .csv files and visualizations as .png files.
Licenses for source code corresponding to:
D. Stutz, A. Hermans, B. Leibe. Superpixels: An Evaluation of the State-of-the-Art. Computer Vision and Image Understanding, 2018.
Note that the two provided images are taken from the BSDS500.
Copyright (c) 2014-2018 David Stutz, RWTH Aachen University
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