Normalized Joint Mutual Information Measure for Image Segmentation Evaluation with Multiple Ground-Truth Images
Supervised or ground-truth-based image segmentation evaluation paradigm plays an important role in objectively evaluating segmentation algorithms. So far, many evaluation methods in terms of comparing clusterings in machine learning field have been developed. Being different from recognition task, image segmentation is considered an ill-defined problem. In a hand-labeled segmentations dataset, for the same image, different human subjects always produce various segmented results, leading to more than one ground-truth segmentations for an image. Thus, it is necessary to extend the traditional pairwise similarity measures that compare a machine generated clustering and a “true” clustering to handle multiple ground-truth clusterings. In this paper, based on the Normalized Mutual Information (NMI) which is a popular information theoretic measure for clustering comparison, we propose to utilize the Normalized Joint Mutual Information (NJMI), an extension of the NMI, to achieve the goal mentioned above. We illustrate the effectiveness of NJMI for objective segmentation evaluation with multiple ground-truth segmentations by testing it on images from Berkeley segmentation dataset.
Keywordsimage segmentation evaluation similarity measure joint mutual information
Unable to display preview. Download preview PDF.
- 1.Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: ICCV (2001)Google Scholar
- 5.Meilǎ, M.: Comparing Clusterings by the Variation of Information. In: Conf. Learning Theory (2003)Google Scholar
- 6.Jiang, X., Marti, C., Irniger, C., Bunke, H.: Distance Measures for Image Segmentation Evaluation. EURASIP Journal on Applied Signal Processing, 1–10 (2006)Google Scholar
- 11.Ge, F., Wang, S., Liu, T.: New Benchmark for Image Segmentation Evaluation. Journal of Electronic Imaging 16(3) (2007)Google Scholar