Elements of information theory
Elements of information theory
An experimental comparison of model-based clustering methods
Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
Multi-information ensemble diversity
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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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 illdefined 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.