A Metric For Distributions With Applications To Image Databases

Brie

5 Jan, 2019

101109iccv1998710701 a metric for distributions with applications to image databases at articlerubner1998amf titlea metric for distributions with applications to image databases authoryossi rubner and carlo tomasi and leonidas j. Proceedings of the 1998 ieee international conference on computer vision bombay india january 1998 pages 59 66postscript 772k pdf 521k.

a metric for distributions with applications to image databases

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The emd also allows for partial matching.

A metric for distributions with applications to image databases. A metric for distributions with applications to image databases. 1 in tro duction multidimensional distributions are often used in computer vision to describ e and summarize di eren t fea tures of an image. The emd also allows for partial matching.

When used to compare distributions that have the same overall mass the emd is a true metric and has easy to compute lower bounds. In this paper we focus on applications to image databases especially color and texture. When used to compare distributions that have the same overall mass the emd is a true metric and has easy to compute lower bounds.

Guibas and carlo tomasi. In this paper we focus on applications to image databases especially color and texture. The wasserstein metric aka earth movers distance.

Guibas computer science department stanford university stanford ca 94305 rubnertomasiguibas at csstanfordedu abstract proceedings of the 1998 ieee international conference on computer vision bombay india. Feature comparisons of vector fields using earth movers distance. A metric for distributions with applications to image databases.

Yossi rubner leonidas j. When used to compare distributions that have the same overall mass the emd is a true metric and has easy to compute lower bounds. A metric for distributions with applications to image databases yossi rubner carlo tomasi and leonidas j.

In this paper we focus on applications to image databases. This paper empirically compares nine families of image dissimilarity measures that are based on distributions of color and texture features summarizing over 1000 cpu hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color and by an image partitioning method for texture.

In this pap er w e fo cus on applications to color and texture and w e compare the retriev al p erformance of the emd with that of other distances. Guibas journalsixth international conference on computer vision ieee cat. Erall mass the emd is a true metric.

A metric for distributions with applications to image databases.

A Metric For Distributions With Applications To Image Databases