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Segmentation Similarity and AgreementSegmentation Evaluation Metric • October 7th, 2018
We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmen- tations as the proportion of boundaries that are not transformed when comparing them us- ing edit distance, essentially using edit dis- tance as a penalty function and scaling penal- ties by segmentation size. We propose several adapted inter-annotator agreement coefficients which use S that are suitable for segmenta- tion. We show that S is configurable enough to suit a wide variety of segmentation evalua- tions, and is an improvement upon the state of the art. We also propose using inter-annotator agreement coefficients to evaluate automatic segmenters in terms of human performance.
