基于NPR的自动多标签GrabCut自然图像分割分析

D. Khattab, H. M. Ebeid, M. Tolba, A. S. Hussein
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引用次数: 0

摘要

自动多标签GrabCut是标准GrabCut技术的扩展,可以自动将给定图像分割成其自然片段,而无需任何用户干预。归一化概率兰德(NPR)指数能够通过比较不同的图像和同一图像的不同分割来给出有意义的比较。本文利用NPR指标对所开发的自动多标签抓取切割的效率进行了进一步的分析。基于使用多个人类地面真理,在伯克利自然图像基准的大规模上进行分割。NPR、PR和GCE指标产生了可接受的精度测量,强调了所提出的技术在大规模数据集上的可扩展性。对不同的图像进行了比较,实验表明,与其他指标相比,NPR是确定良好分割的最有效分数。
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Analysis of Automatic Multi-label GrabCut using NPR for natural image segmentation
Automatic Multi-label GrabCut is an extension of the standard GrabCut technique to segment a given image automatically into its natural segments without any user intervention. The Normalized Probabilistic Rand (NPR) index is able to give meaningful comparisons by comparing different images and different segmentations of the same image. In this paper, more analysis is conducted to evaluate the efficiency of the developed automatic multi-label GrabCut using the NPR index. Based on using more than one human ground truth, segmentations are conducted on a large scale of the Berkeley's benchmark of natural images. The NPR, PR and GCE metrics produced acceptable accuracy measures emphasizing the scalability of the proposed technique for large scale datasets. Comparisons are applied for different images and experiments show that the NPR is the most efficient score to determine good segmentation compared to other metrics.
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