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arXiv · 1806.08741

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

Bradley C. Lowekamp·David T. Chen·Ziv Yaniv·Terry S. Yoo
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Abstract

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).

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Bradley C. Lowekamp, David T. Chen, Ziv Yaniv, Terry S. Yoo. 2018-06-22. Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach. https://arxiv.org/abs/1806.08741

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