On Selecting the Best Unsupervised Evaluation Techniques for Image Segmentation

Abstract

One fundamental difficulty with evaluation of segmentation is that there is no objective, clear definition of good or bad segmentation. Even worse, different observers often do not agree on how to segment the same image. In this paper, we present six unsupervised metrics in the literature that are commonly used to evaluate segmentation results. Then we… (More)

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Cite this paper

@inproceedings{Duong2016OnST, title={On Selecting the Best Unsupervised Evaluation Techniques for Image Segmentation}, author={Trung H. Duong and Lawrence L. Hoberock}, year={2016} }