How to use flann based matcher, or generally flann in opencv?

http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_matchers.html#flannbasedmatcher

Please can somebody show me sample code or tell me how to use this class and methods. I just want开发者_运维百科 to match SURF's from a query image to those with an image set by applying Flann. I have seen many image match code in the samples but what still eludes me is a metric to quantify how similar an image is to other. Any help will be much appreciated.

Here's untested sample code

using namespace std;
using namespace cv;

Mat query; //the query image
vector<Mat> images;   //set of images in your db

/* ... get the images from somewhere   ... */

vector<vector<KeyPoint> > dbKeypoints;
vector<Mat> dbDescriptors;

vector<KeyPoint> queryKeypoints;
Mat queryDescriptors;

/* ... Extract the descriptors ... */

FlannBasedMatcher flannmatcher;

//train with descriptors from your db
 flannmatcher.add(dbDescriptors);
 flannmatcher.train();

vector<DMatch > matches;

flannmatcher.match(queryDescriptors, matches);

/* for kk=0 to matches.size()

       the best match for queryKeypoints[matches[kk].queryIdx].pt 
       is dbKeypoints[matches[kk].imgIdx][matches[kk].trainIdx].pt

 */

Finding the most 'similar' image to the query image depends on your application. Perhaps the number of matched keypoints is adequate. Or you may need a more complex measure of similarity.

To reduce the number of false positives, you can compare the first most nearest neighbor to the second most nearest neighbor by taking the ratio of there distances. distance(query,mostnearestneighbor)/distance(query,secondnearestneighbor) < T, the smaller the ratio is, the higher the distance of the second nearest neighbor to the query descriptor. This thus is a translation of high distinctiveness. Used in many computer vision papers that envision registration.