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

Author Affiliations +

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE