Vectorized implementation of K-means

Tomoki Otsuka; Norishige Fukushima

doi:10.1117/12.2590842

13 March 2021 Vectorized implementation of K-means

Tomoki Otsuka, Norishige Fukushima

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 1176631 (2021) https://doi.org/10.1117/12.2590842
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

K-means is a widely used clustering technique that seeks to minimize the squared distance among points in the same cluster. K-means is an appealing clustering method in terms of computational speed. Also, K-means is the simplest clustering; thus, researchers select K-means as a first choice. Therefore, accelerating K-means is essential. In this study, we transform data structure from the array of structures to the structure of arrays for accelerating K-means by SIMD vectorization. Experimental results show that our implementation is faster than OpenCV’s implementation.

Citation Download Citation

Tomoki Otsuka and Norishige Fukushima "Vectorized implementation of K-means", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176631 (13 March 2021); https://doi.org/10.1117/12.2590842

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