Discriminative Learning of Local Image Descriptors

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G-block T-block G-block: Gaussian smoothing. S-block/ E-block N-block T-block: Non-linear transformation to each sample grid in smoothed patch. S-block/E-block: Spatial pooling <strong>of</strong> the T-block responses. S-block uses parameterized pooling regions, E-block is non-parametric. N-block: SIFT-style Normalization.
G-block T-block G-block: Gaussian smoothing. S-block/ E-block N-block T-block: Non-linear transformation to each sample grid in smoothed patch. S-block/E-block: Spatial pooling <strong>of</strong> the T-block responses. S-block uses parameterized pooling regions, E-block is non-parametric. N-block: SIFT-style Normalization.
Page 1 and 2: Discriminative Learning of Local Im
Page 3 and 4: Matthew Brown 作者简介 Postdoc
Page 5 and 6: 文章出处文章信息 PAMI 201
Page 7 and 8: 作者信息文章信息提纲
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 13 and 14: G-block T-block G-block: Gaussian s
Page 15: 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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