A Probabilistic Measure for Quantitative Evaluation of Image Segmentation

In this letter, we propose a probabilistic measure to evaluate the machine segmentation with multiple ground truths. The measure is designed for adaptively evaluating the structural information extracted from the segmentations. This induces a local similarity score at every point in the segmentation and can in turn be accumulated in a principled information-theoretic way into a global similarity score of the entire segmentation. Experiments are conducted on benchmark images from the Berkeley segmentation database and our own database. Results show that the proposed measure can faithfully reflect the perceptual qualities of the segmentations.