Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise

Brekhna Brekhna; Arif Mahmood; Yuanfeng Zhou; Caiming Zhang

doi:10.1117/1.JEI.26.6.061604

24 August 2017 Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise

Brekhna Brekhna, Arif Mahmood, Yuanfeng Zhou, Caiming Zhang

Author Affiliations +

Funded by: Natural Science Foundation of China, NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization, Key Research and Development Project of Shandong Province

Journal of Electronic Imaging, Vol. 26, Issue 6, 061604 (August 2017). https://doi.org/10.1117/1.JEI.26.6.061604

Abstract

Superpixels have gradually become popular in computer vision and image processing applications. However, no comprehensive study has been performed to evaluate the robustness of superpixel algorithms in regard to common forms of noise in natural images. We evaluated the robustness of 11 recently proposed algorithms to different types of noise. The images were corrupted with various degrees of Gaussian blur, additive white Gaussian noise, and impulse noise that either made the object boundaries weak or added extra information to it. We performed a robustness analysis of simple linear iterative clustering (SLIC), Voronoi Cells (VCells), flooding-based superpixel generation (FCCS), bilateral geodesic distance (Bilateral-G), superpixel via geodesic distance (SSS-G), manifold SLIC (M-SLIC), Turbopixels, superpixels extracted via energy-driven sampling (SEEDS), lazy random walk (LRW), real-time superpixel segmentation by DBSCAN clustering, and video supervoxels using partially absorbing random walks (PARW) algorithms. The evaluation process was carried out both qualitatively and quantitatively. For quantitative performance comparison, we used achievable segmentation accuracy (ASA), compactness, under-segmentation error (USE), and boundary recall (BR) on the Berkeley image database. The results demonstrated that all algorithms suffered performance degradation due to noise. For Gaussian blur, Bilateral-G exhibited optimal results for ASA and USE measures, SLIC yielded optimal compactness, whereas FCCS and DBSCAN remained optimal for BR. For the case of additive Gaussian and impulse noises, FCCS exhibited optimal results for ASA, USE, and BR, whereas Bilateral-G remained a close competitor in ASA and USE for Gaussian noise only. Additionally, Turbopixel demonstrated optimal performance for compactness for both types of noise. Thus, no single algorithm was able to yield optimal results for all three types of noise across all performance measures. Conclusively, to solve real-world problems effectively, more robust superpixel algorithms must be developed.

Citation Download Citation

Brekhna Brekhna, Arif Mahmood, Yuanfeng Zhou, and Caiming Zhang "Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise," Journal of Electronic Imaging 26(6), 061604 (24 August 2017). https://doi.org/10.1117/1.JEI.26.6.061604

Received: 1 April 2017; Accepted: 26 July 2017; Published: 24 August 2017

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