Discriminative Learning of Local Image Descriptors

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摘要 A realistic ground truth dataset of matched patches based on multi-view stereo data Describe a set of building blocks for constructing descriptors Parametric learning for local image descriptors Non-Parametric learning for local image descriptors Dimensionality reduction Obtain descriptors that exceed the state-of-the-art performance with lower dimensionality
作者信息文章信息提纲拟解决的问题与采用的思路本文的方法实验结论
Page 1 and 2: Discriminative Learning of Local Im
Page 3 and 4: Matthew Brown 作者简介 Postdoc
Page 5: 文章出处文章信息 PAMI 201
Page 9 and 10: 作者信息文章信息提纲
Page 11 and 12: G-block T-block G-block: Gaussian s
Page 19 and 20: G-block T-block S-block/ E-block N-
Page 21 and 22: G-block T-block S-block S-block/ E-
Page 23 and 24: Learning Parametric Descriptors Par
Page 25 and 26: Input: Learning Non-Parametric Desc
Page 27 and 28: 1 2 3 Learning Non-Parametric 0 1 D
Page 29 and 30: Learning Non-Parametric Descriptors
Page 31 and 32: 实验数据:大约250万标记好
Page 33 and 34: Parametric Descriptors
Page 35 and 36: Parametric Descriptors
Page 37 and 38: Non-Parametric Descriptors
Page 39 and 40: Non-Parametric Descriptors
Page 41 and 42: Dimension Reduced Parametric Descri
Page 43 and 44: Dimension Reduced Parametric Descri
Page 45 and 46: Effects of Normalization r/sqrt(D)
Page 47 and 48: 结论 The techniques have been use
Page 49 and 50: 结论 Recommendations by the autho

Discriminative Learning of Local Image Descriptors

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