# Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach

arXiv: https://arxiv.org/abs/1806.08741 · HTML: https://mlsanity.com/paper/1806.08741/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms](https://mlsanity.com/paper/2405.07762/) (2024)

## Abstract (arXiv)

Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a generalized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative implementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).