A Ground-Truth Fusion Method for Image Segmentation Evaluation

Sree Ramya S. P. Malladi, Sundaresh Ram, Jeffrey J. Rodríguez
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引用次数: 2

Abstract

Image segmentation evaluation is popularly categorized into two different approaches based on whether the evaluation uses a human expert’s manual segmentation as a reference or not. When comparing automated segmentation against manual segmentation, also referred to as the ground-truth segmentation, multiple ground-truths are usually available. Much research has been done on analysis of segmentation algorithms and performance metrics, but very little study has been done on analyzing techniques for ground-truth fusion from multiple ground-truth segmentations. We propose a hybrid ground-truth fusion technique for image segmentation evaluation and compare it with other existing ground-truth fusion methods on a data set having multiple ground-truths at various coarseness levels. Qualitative and quantitative results show that the proposed method provides improved segmentation evaluation performance.
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一种基于真值融合的图像分割评价方法
基于评估是否使用人类专家的人工分割作为参考,通常将图像分割评估分为两种不同的方法。当比较自动分割和手动分割(也称为基础事实分割)时,通常有多个基础事实可用。对分割算法和性能指标的分析研究较多,但对多段真值分割的真值融合分析技术的研究很少。我们提出了一种用于图像分割评估的混合真值融合技术,并将其与现有的在不同粗糙程度下具有多个真值的数据集上的其他真值融合方法进行了比较。定性和定量结果表明,该方法具有较好的分割评价性能。
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