On minimum variance thresholding

Authors:
Z. Hou;Q. Hu;W. L. Nowinski
Affiliations:
Department of Interactive Media, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore;Biomedical Imaging Lab, Singapore Bioimaging Consortium, 11 Biopolis Way, #02-02 Helios, Singapore 138667, Singapore;Biomedical Imaging Lab, Singapore Bioimaging Consortium, 11 Biopolis Way, #02-02 Helios, Singapore 138667, Singapore
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

Variance-based thresholding methods could be biased from the threshold found by expert and the underlying mechanism responsible for this bias is explored in this paper. An analysis on the minimum class variance thresholding (MCVT) and the Otsu method, which minimizes the within-class variance, is carried out. It turns out that the bias for the Otsu method is due to differences in class variances or class probabilities and the resulting threshold is biased towards the component with larger class variance or larger class probability. The MCVT method is found to be similar to the minimum error thresholding.