Crate graph_based_image_segmentation

Efficient Graph-Based Image Segmentation

This repository contains a Rust implementation of the graph-based image segmentation algorithms described in \c[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

use opencv::imgcodecs::{imread, IMREAD_COLOR};
use graph_based_image_segmentation::{Segmentation, EuclideanRGB, NodeMergingThreshold};

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

    let threshold = 10f32;
    let segment_size = 10;
    let mut segmenter = Segmentation::new(
        EuclideanRGB::default(),
        NodeMergingThreshold::new(threshold),
        segment_size,
    );

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

Structs

• EuclideanRGB
  Euclidean RGB distance.
• ImageNodeColor
  Represents a pixel in a video. Each pixel is represented by its color which is needed to compute the weights between pixels.
• ManhattanRGB
  Manhattan (i.e. L1) distance.
• NodeMergingThreshold
  The original criterion described in
• Segmentation
  Implementation of graph based image segmentation as described in the paper by Felzenswalb and Huttenlocher.

Traits

• Distance
  Trait to be implemented by a concrete distance. The distance defines how the weights between nodes in the image graph are computed. See the paper by Felzenswalb and Huttenlocher for details.
• NodeMerging
  The magic part of the graph segmentation, i.e. s given two nodes decide whether to add an edge between them (i.e. merge the corresponding segments). See the paper by Felzenswalb and Huttenlocher for details.