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Noise-robust SLIC superpixel for natural images
Dong L.2; Zhou J.2
2017-07-13
Conference Name7th International Conference on Cloud Computing and Big Data (CCBD)
Source PublicationProceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Pages335-340
Conference DateNOV 16-18, 2016
Conference PlaceMacau, PEOPLES R CHINA
Abstract

Superpixel algorithm aims to semantically group neighboring pixels into a coherent region. It could significantly boost the performance of the subsequent vision processing task such as image segmentation. Recently, the work simple linear iterative clustering (SLIC) [1] has drawn huge attention for its state-of-the-art segmentation performance and high computational efficiency. However, the performance of SLIC is dramatically degraded for noisy images. In this work, we propose three measures to improve the robustness of SLIC against noise: 1) a new pixel intensity distance measurement is designed by explicitly considering the within-cluster noise variance; 2) the spatial distance measurement is refined by exploiting the variation of pixel locations in a cluster; and 3) a noise-robust estimator is proposed to update the cluster centers by excluding the possible outliers caused by noise. Extensive experimental results on synthetic noisy images validate the effectiveness of those improvements. In addition, we apply the proposed noise-robust SLIC to superpixel-based noise level estimation task to demonstrate its practical usage.

KeywordNoise Robust Slic Superpixel
DOI10.1109/CCBD.2016.072
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems
WOS IDWOS:000431860300061
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.UMacau Zhuhai Research Institute
2.Universidade de Macau
Recommended Citation
GB/T 7714
Dong L.,Zhou J.. Noise-robust SLIC superpixel for natural images[C],2017:335-340.
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