The Earth Mover's Distance as a metric for image retrieval
The Earth Mover's Distance as a metric for image retrieval
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DOI：
10.1023/a:1026543900054
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发表时间：
2000-11-01
影响因子：
19.5
通讯作者：
Guibas, LJ
中科院分区：
文献类型：
--
作者：
Rubner, Y;Tomasi, C;Guibas, LJ
我们调查的两个分布，地球移动器的距离（EMD），基于内容的图像检索之间的度量的属性。EMD的基础上，必须支付的最小成本将一个分布转换成其他的，在精确的意义上，并首次提出了某些视觉问题的Peleg，沃曼，和ROM。对于图像检索，我们联合收割机这个想法与一个表示方案的分布是基于矢量量化。这种组合导致图像比较框架，其通常比其他先前提出的方法更好地考虑感知相似性。EMD是基于从线性优化的运输问题的解决方案，有效的算法是可用的，并且还允许自然地部分匹配。它比直方图匹配技术更鲁棒，因为它可以对分布的可变长度表示进行操作，从而避免量化和直方图的其他典型合并问题。当用于比较具有相同总质量的分布时，EMD是真正的度量。在本文中，我们专注于颜色和纹理的应用，我们比较的EMD与其他距离的检索性能。
We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.