Fast-convergence superpixel algorithm via an approximate optimization

Kensuke Nakamura; Byung-Woo Hong

doi:10.1117/1.JEI.25.5.053035

24 October 2016 Fast-convergence superpixel algorithm via an approximate optimization

Kensuke Nakamura, Byung-Woo Hong

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 053035 (October 2016). https://doi.org/10.1117/1.JEI.25.5.053035

Abstract

We propose an optimization scheme that achieves fast yet accurate computation of superpixels from an image. Our optimization is designed to improve the efficiency and robustness for the minimization of a composite energy functional in the expectation–minimization (EM) framework where we restrict the update of an estimate to avoid redundant computations. We consider a superpixel energy formulation that consists of L²-norm for the spatial regularity and L¹-norm for the data fidelity in the demonstration of the robustness of the proposed algorithm. The quantitative and qualitative evaluations indicate that our superpixel algorithm outperforms SLIC and SEEDS algorithms. It is also demonstrated that our algorithm guarantees the convergence with less computational cost by up to 89% on average compared to the SLIC algorithm while preserving the accuracy. Our optimization scheme can be easily extended to other applications in which the alternating minimization is applicable in the EM framework.

Citation Download Citation

Kensuke Nakamura and Byung-Woo Hong "Fast-convergence superpixel algorithm via an approximate optimization," Journal of Electronic Imaging 25(5), 053035 (24 October 2016). https://doi.org/10.1117/1.JEI.25.5.053035

Published: 24 October 2016

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

Includes PDF, HTML & Video, when available