如何使用PCA缩小尺寸 [英] How to Use PCA to Reduce Dimension

问题描述

输入: LBP是从尺寸为75520的图像中提取的特征，因此输入的LBP数据包含1行和75520列.

Input : LBP Feature extracted from an image with dimension 75520, so the input LBP data contains 1 row and 75520 columns.

必需的输出:在输入上应用PCA以减小尺寸，

Required Output: Apply PCA on input to reduce the dimension,

当前我的代码如下，

void PCA_DimensionReduction(Mat &src, Mat &dst){

    int PCA_DIMENSON_VAL 40
    Mat tmp = src.reshape(1,1); //1 rows X 75520 cols
    Mat projection_result;
    Mat input_feature_vector;
    Mat norm_tmp;
    normalize(tmp,input_feature_vector,0,1,NORM_MINMAX,CV_32FC1);
    PCA pca(input_feature_vector,Mat(),CV_PCA_DATA_AS_ROW, PCA_DIMENSON_VAL);
    pca.project(input_feature_vector,projection_result);
    dst = projection_result.reshape(1,1);
}

基本上，我正在使用此功能来匹配两个图像之间的相似性，但是由于没有应用PCA，我无法获得正确的结果.

Basically I am using this features to match similarity between two images, but I am not getting proper result as without applying PCA.

任何帮助将不胜感激...

Any help will be appreciated...

致谢

哈里斯...

推荐答案

，您将不得不从很多图像中收集特征向量，从中(离线)制作一个pca，然后再使用均值&投影的特征向量.

you will have to collect feature vectors from a lot of images, make a single pca from that (offline), and later use the mean & eigenvectors for the projection.

// let's say, you have collected 10 feature vectors a 30 elements.
// flatten them to a single row (reshape(1,1)) and push_back into a big Data Mat

Mat D(10,30,CV_32F); // 10 rows(features) a 30 elements
randu(D,0,10);       // only for the simulation here
cerr << D.size() << endl;
// [30 x 10]


// now make a pca, that will only retain 6 eigenvectors
// so the later projections are shortened to 6 elements:

PCA p(D,Mat(),CV_PCA_DATA_AS_ROW,6);
cerr << p.eigenvectors.size() << endl;
// [30 x 6]

// now, that the training step is done, we can use it to
// shorten feature vectors:
// either keep the PCA around for projecting:

// a random test vector, 
Mat v(1,30,CV_32F);
randu(v,0,30);

// pca projection:
Mat vp = p.project(v);

cerr << vp.size() << endl;
cerr << vp << endl;
// [6 x 1]
// [-4.7032223, 0.67155731, 15.192059, -8.1542597, -4.5874329, -3.7452228]


// or, maybe, save the pca.mean and pca.eigenvectors only, and do your own projection:

Mat vp2 = (v - mean) * eigenvectors.t();

cerr << vp2.size() << endl;
cerr << vp2 << endl;
//[6 x 1]
//[-4.7032223, 0.67155731, 15.192059, -8.1542597, -4.5874329, -3.7452228]

好吧，这是不利的一面:从4.4k火车图像计算pca，75k特征元素将像是美好的一天；)

well, oh, here's the downside: calculating a pca from 4.4k train images a 75k feature elements will take like a good day ;)

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