#graphics #image-processing


An implementation of graph-based image segmentation algorithms based on superpixels

3 unstable releases

0.2.1 May 25, 2023
0.2.0 May 24, 2023
0.1.0 Nov 4, 2022

#450 in Images

MIT license

457 lines

Efficient Graph-Based Image Segmentation

This repository contains a Rust implementation of the graph-based image segmentation algorithms described in [1] (available here) focussing on generating over-segmentations, also referred to as superpixels.

Contours Labels

Please note that this is a reference implementation and not particularly fast.

[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 is based on this work by David Stutz, which in turn 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.

Example use

fn main() {
    let mut image = imread("data/tree.jpg", IMREAD_COLOR).unwrap();

    let threshold = 10f32;
    let segment_size = 10;
    let mut segmenter = Segmentation::new(

    // NOTE: The image should be blurred before use; this is left out here for brevity.
    let labels = segmenter.segment_image(&image);


~23K SLoC