如何通过减小特征维度来改善LBP算子 [英] how to improve LBP operator by reducing feature dimension

问题描述

我使用 LBP 与MATLAB进行提取功能，但准确度太低

如何减少LBP中的功能箱？

非常感谢。

解决方案

使用 pcares 功能。 pcares 代表 PCA剩余：

  [残差，重构] = pcares（X，ndim）; 
  

residuals 返回通过保留 ndim n-by-p 矩阵的主要组件 X 。 X 是数据矩阵或包含您的数据的矩阵。 X 的行对应于观察值，列是变量。 ndim 是一个标量，必须小于或等于 p 。 residuals 是与 X 相同大小的矩阵。

<基于 ndim 输入，p> reconstruct 将具有缩减的数据。请注意， reconstruct 仍然在原始维度中为 X 。因此，您可以选择第一个 ndim 列，这将对应于使用由 ndim 。换句话说：

  reduced = reconstruction（：，1：ndim）; 
  

因此， reduced 尺寸缩小为 ndim 尺寸。

小笔记

您需要统计工具箱才能运行 pcares 。如果你不这样做，那么这种方法将不起作用。

I am using LBP with MATLAB for extraction feature but the accuracy is too low

how to reduce the feature bins in LBP?

many thanks.

解决方案

Use the pcares function to do that. pcares stands for PCA Residuals:

[residuals, reconstructed] = pcares(X, ndim);

residuals returns the residuals obtained by retaining ndim principal components of the n-by-p matrix X. X is the data matrix, or the matrix that contains your data. Rows of X correspond to observations and columns are the variables. ndim is a scalar and must be less than or equal to p. residuals is a matrix of the same size as X.

reconstructed will have the reduced dimensional data based on the ndim input. Note that reconstructed will still be in the original dimension as X. As such, you can choose the first ndim columns and this will correspond to those features constructed using the number of the dimensions for the feature specified by ndim. In other words:

reduced = reconstructed(:,1:ndim);

As such, reduced will contain your data that was dimension reduced down to ndim dimensions.

Small Note

You need the Statistics Toolbox in order to run pcares. If you don't, then this method won't work.

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