24 October 2016 Fast-convergence superpixel algorithm via an approximate optimization
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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.
© 2016 SPIE and IS&T
Kensuke Nakamura, Kensuke Nakamura, Byung-Woo Hong, 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 . Submission:
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