Improve the accuacy of image matching algorithm?

edit

I am new to openCV. I am going to write a program to retrieve the best matched image from the image database given an input image. However, I found that the accuracy is quite low. A low percentage of images can be correctly matched from the database. The program can run, but I am not satisfied with that.

What are the problems in the algorithm? Which parts do I need to improve? And how can I improve it? Thanks guys.

 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"  
 #include <iostream>  
 #include <stdio.h>  
 #include <algorithm>  

using namespace std;
using namespace cv;

#define IMAGE_LIST_FILE "inputimage.txt"

/**
 * @function main
 */

//Compute pixel-by-pixel difference
double compareImgs(Mat img1, Mat img2)
{
    int w = img1.cols, h = img2.rows;
    Mat new_img2;
    resize(img2, new_img2, img1.size());
    double sum = 0;
    for (int i = 0; i < w; i++)for (int j = 0; j < h; j++)
    {
        sum += abs(img1.at<uchar>(j, i) - new_img2.at<uchar>(j, i));
    }
    return sum;
}

int main( int argc, char** argv )

{

Mat src_input, gray_input;  
Mat db_img, db_gray_img;

double score[1000];
int db_id = 0;
double maxscore = 1000000000;
int maxscore_num;
char maximg_name[200];
Mat max_img;

src_input = imread("dinosaur.jpg"); // read input image
if( !src_input.data )
{
    printf("Cannot find the input image!\n");
    system("pause");
    return -1; 
}
imshow("Input",src_input);
/// Convert to grayscale
cvtColor(src_input, gray_input, COLOR_BGR2GRAY);

///Read Database
FILE   *fp; 
char imagepath[200];
fp   =   fopen( IMAGE_LIST_FILE, "r ");
printf("Extracting features from input images...\n");
while(!feof(fp))
{
   while(fscanf(fp,   "%s ",   imagepath)   >   0) 
   {
       printf("%s\n",imagepath);
       char tempname[200];
       sprintf_s(tempname,200,"../%s",imagepath);

       db_img = imread(tempname); // read database image
       if( !db_img.data )
       {
           printf("Cannot find the database image number %d!\n",db_id+1);
           system("pause");
           return -1; 
       }

       cvtColor(db_img, db_gray_img, COLOR_BGR2GRAY);

       /// Apply the pixel-by-pixel comparison method
       score[db_id] = compareImgs(gray_input, db_gray_img);
       /// Compute max score
       if(score[db_id]<maxscore)
       {
           maxscore=score[db_id];
           maxscore_num = db_id;
           memcpy(maximg_name,tempname,200*sizeof(char));
       }
       db_id++; 
   }
} 
fclose(fp);

Mat maximg = imread(maximg_name);
imshow("Best Match Image",maximg);

printf("the most similar image is %d, the pixel-by-pixel difference is %f\n",maxscore_num+1, maxscore);

printf( "Done \n" );
// Wait for the user to press a key in the GUI window.
//Press ESC to quit
int keyValue=0;
while(keyValue>=0)
{
    keyValue=cvWaitKey(0);

    switch(keyValue)
    {
    case 27:keyValue=-1;
        break;  
    }
}

return 0;

}