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In this paper, we propose a thresholding method utilizing the mutual relation between the two used features, which should be both robust and of low correlation. The mutual relation is exploited through a histogram subtraction transformation, which tries to reduce only the background part of each of the histograms for the features so that we can easily differentiate between the background part and the target part. A speedup scheme that requires prior knowledge to exclude the possible local minimum(s) is also presented. To sufficiently validate the effectiveness of our histogram subtraction transformation, the two developed thresholding methods are applied to shadow detection and vegetation detection and compared with some state-of-the-art methods in both fields. The comparative results on multiple data sets indicate that with the thresholds automatically provided by the proposed thresholding methods, even the simple binarization methods can obtain good detection results. [ABSTRACT FROM AUTHOR]