如何在opencv中将SIFT功能用于单词袋? [英] how to use SIFT features for bag of words in opencv?

问题描述

在阅读了图像的筛选特征后，我已经阅读了很多有关实现单词袋的文章，但是我仍然对下一步的工作感到困惑.我具体要做什么?

I have read a lot of articles about implementing bag of words after taking sift features of an image, but I'm still confused what to do next. What do i specifically do?

非常感谢您的指导.

这是我到目前为止的代码.

This is the code that i have so far.

cv::Mat mat_img = cropped.clone();
Mat grayForML;
cvtColor(mat_img, grayForML, CV_BGR2GRAY);
IplImage grayImageForML = grayForML.operator IplImage();


//create another copy of iplGray
IplImage *input = cvCloneImage(&grayImageForML);
Mat matInput = cvarrToMat(input);
//  Mat matInput = copy_gray.clone();
cv::SiftFeatureDetector detector;
std::vector<cv::KeyPoint> keyPoints;
detector.detect(input, keyPoints);
//add results to image and save.
cv::Mat output;
cv::drawKeypoints(input, keyPoints, output);    //SIFT OUTPUT RESULT


//resize and display
cv::Mat output_reduced;
cv::resize(output, output_reduced, cv::Size2i(output.cols / 2, output.rows / 2));


imshow("SIFT result", output_reduced);

推荐答案

训练一揽子单词系统的过程如下:

Training a bag of words system goes as follows:

1. 为训练集的每个图像计算特征
2. 群集这些功能
3. 用每个群集中具有特征的图像标记每个群集

此时培训已经完成，您可以按照以下步骤进行测试:

At this point the training is done and you can start with the testing as follows:

1. 计算测试图像的特征
2. 对于每个功能，找到最近的群集
3. 为属于该群集的每个训练图像添加一个勾号
4. 重复测试图像的所有功能
5. 滴答声最高的图像是最佳匹配，而滴答声次高的图像是次佳匹配，依此类推

您会注意到，使用SIFT没有限制.您可以尝试使用其他特征提取器和描述符.

As you can notice, there is no restriction to using SIFT. You can try different feature extractors and descriptors.

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