how to find matching image

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Hello,

i want to extract similar image from vocabulary that was created in above code.this code run successfully creating two descriptors file.My question is that I don't understand that file "descriptr.yml" contains extracted features of query image or matched features of query image with vocabulary. Please help

my code is...

#include "stdafx.h"
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <opencv2/nonfree/features2d.hpp>

using namespace cv;
using namespace std;

#define DICTIONARY_BUILD 0 // set DICTIONARY_BUILD 1 to do Step 1, otherwise it goes to step 2

int _tmain(int argc, _TCHAR* argv[])
{   
int minHessian = 400; //Hessian Threshold
#if DICTIONARY_BUILD == 1

    //Step 1 - Obtain the set of bags of features.

    //to store the input file names
char  filename[100]={};     
    //to store the current input image
    Mat input;  

    //To store the keypoints that will be extracted by SURF
    vector<KeyPoint> keypoints;
    //To store the SURF descriptor of current image
    Mat descriptor;
    //To store all the descriptors that are extracted from all the images.
    Mat featuresUnclustered;
    //The SURF feature extractor and descriptor 
    SurfDescriptorExtractor detector(minHessian,4,2,false);     

    //I select 20 (1000/50) images from 1000 images to extract feature descriptors and build the vocabulary
    for(int f=1;f<25;f++){      
        //create the file name of an image
        sprintf(filename,"C:\\harshada\\OpenCV BoFSURF\\Release\\image\\%i.jpg",f);
        //open the file
        input = imread(filename, CV_LOAD_IMAGE_GRAYSCALE); //Load as grayscale  
    //Mat img =cv::imread("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\22.jpg");
    //cv::cvtColor(img,img,CV_BGR2GRAY);
        if(input.empty())
    {
        cout << "Error: Image cannot be loaded !" << endl;
        system("Pause");
        return -1;
    }
        //detect feature points
        detector.detect(input, keypoints);
        //compute the descriptors for each keypoint
        detector.compute(input, keypoints,descriptor);      
        //put the all feature descriptors in a single Mat object 
        featuresUnclustered.push_back(descriptor);      
        //print the percentage
        printf("%i percent done\n",f/10);
    }   


    //Construct BOWKMeansTrainer
    //the number of bags
    int dictionarySize=200;
    //define Term Criteria
    TermCriteria tc(CV_TERMCRIT_ITER,100,0.001);
    //retries number
    int retries=1;
    //necessary flags
    int flags=KMEANS_PP_CENTERS;
    //Create the BoW (or BoF) trainer
    BOWKMeansTrainer bowTrainer(dictionarySize,tc,retries,flags);
    bowTrainer.add(featuresUnclustered);
    if (featuresUnclustered.type() != CV_32F)
{
    featuresUnclustered.convertTo(featuresUnclustered, CV_32F);
}
Mat vocabulary = bowTrainer.cluster();
    //cluster the feature vectors
    //Mat dictionary=bowTrainer.cluster(featuresUnclustered);   
    //store the vocabulary
    FileStorage fs("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\dictionary.yml", FileStorage::WRITE);
    fs << "vocabulary" << vocabulary;
    fs.release();

#else
    //Step 2 - Obtain the BoF descriptor for given image/video frame. 

    //prepare BOW descriptor extractor from the dictionary    
    Mat dictionary; 
    FileStorage fs("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\dictionary.yml", FileStorage::READ);
    fs["vocabulary"] >> dictionary;
    fs.release();   

    //create a nearest neighbor matcher
    Ptr<DescriptorMatcher> matcher(new FlannBasedMatcher);
    //create SURF feature point extracter
    Ptr<FeatureDetector> detector(new SurfFeatureDetector(minHessian,4,2,false));
    //create SURF descriptor extractor
    Ptr<DescriptorExtractor> extractor(new SurfDescriptorExtractor(minHessian,4,2,false));  
    //create BoF (or BoW) descriptor extractor
    BOWImgDescriptorExtractor bowDE(extractor,matcher);
    //Set the dictionary with the vocabulary we created in the first step
    bowDE.setVocabulary(dictionary);

    //To store the image file name
    char * filename = new char[100 ...