Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
This work provides a scalable solution for image segmentation tasks, but it is incremental as it builds on the existing SLIC algorithm with parallelization.
The authors tackled the problem of scaling superpixel algorithms by generalizing the simple linear iterative clustering (SLIC) method for n-dimensional and multi-channel images and implementing a parallel, multi-threaded approach, resulting in good scalability with runtime gains even when using hyperthreading.
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).