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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