simple_clustering

Image segmentation library and command line tool using clustering methods.

Currently supported algorithms are the SLIC (simple linear iterative clustering) and SNIC (simple non-iterative clustering) superpixel algorithms. The crate also supports drawing basic contours around the image segments.

To use as a library, add the following to your Cargo.toml. Executable builds can be found at https://github.com/okaneco/simple_clustering/releases.

[dependencies.simple_clustering]
version = "0.2"
default-features = false

Features

image segmentation using SNIC and SLIC
find mean colors of segmented regions
draw contours around segment boundaries
command line tool for generating segmented images from JPG/PNG input files

Examples

Images are from the Berkeley Segmentation Dataset and Benchmark unless noted otherwise.

SNIC and SLIC

The algorithm can be selected with the -a option. By default, the algorithm is snic.

simple_clustering -i 295087.jpg
simple_clustering -i 295087.jpg -a slic

The suggested number of superpixels to find is specified with -k or -n. Resulting superpixel counts may be slightly greater or less than this value due to characteristics of seeding or enforcing connectivity of superpixel labels. The default setting is 1000.

simple_clustering -i 295087.jpg -k 200

The -m option is used to control the "compactness" of each superpixel. The number ranges from 1 to 20 with a tradeoff between uniform superpixel size and better boundary adherence. 1 will result in larger and more non-uniform superpixels while 20 will result in more compact and uniform superpixels. By default, -m is set to 10.

simple_clustering -i 55067.jpg -m 1
simple_clustering -i 55067.jpg -m 20

Left image: -m 1. Right image: -m 20.

Contours

The segment color defaults to black, #000, but can be specified with the --segment-color option followed by a 3 or 6 digit hexadecimal RGB color. The following example sets the color to gray, #777. To save the segment contours over the original image data, add the --no-mean flag to skip generating the mean color image.

simple_clustering -i 113016.jpg -k 200 --segments --segment-color 777
simple_clustering -i 113016.jpg -k 200 --segments --no-mean

Left image: Segmented image with gray contours. Right image: Superpixel regions drawn over original image data.

Reference

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC Superpixels. EPFL Technical Report no. 149300, June 2010.

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 – 2282, May 2012.

Achanta, R., & Süsstrunk, S. Superpixels and polygons using simple non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.

Martin, D., Fowlkes, C., Tal, D., & Malik, J. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proc. 8th Int'l Conf. Computer Vision, vol. 2, p. 416 – 423, July 2001.

License

This crate is licensed under either

the MIT License, or
the Apache License (Version 2.0)

at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.