variation_of_information(image0=None, image1=None, *, table=None, ignore_labels=())
The variation of information is defined as VI(X,Y) = H(X|Y) + H(Y|X). If X is the ground-truth segmentation, then H(X|Y) can be interpreted as the amount of under-segmentation and H(X|Y) as the amount of over-segmentation. In other words, a perfect over-segmentation will have H(X|Y)=0 and a perfect under-segmentation will have H(Y|X)=0.
Label images / segmentations, must have same shape.
A contingency table built with skimage.evaluate.contingency_table. If None, it will be computed with skimage.evaluate.contingency_table. If given, the entropies will be computed from this table and any images will be ignored.
Labels to ignore. Any part of the true image labeled with any of these values will not be counted in the score.
The conditional entropies of image1|image0 and image0|image1.
Return symmetric conditional entropies associated with the VI.
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