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SURF feature vector keypoints computer similarity percentage

I am following the idea of this point for an image search program (http://www.pyimagesearch.com/2014/12/01/complete-guide-building-image-search-engine-python-opencv/). I got the feature matching with Flann to work and is given an image img_matches with the result drawmatch.

drawMatches( img_1, keypoints_1, img_2, keypoints_2,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

What I want is to take in the two feature vectors vector<keypoint> keypoints_1, keypoints_2; and combine it with good matches vector< DMatch > good_matches; to compute a similarity score in percentage showing how similar the two picture is. Is there a simple similarity or distance metric function that would take in three vectors and help me compute this number?

SURF feature vector keypoints computer similarity percentage

I am following the idea of this point for an image search program (http://www.pyimagesearch.com/2014/12/01/complete-guide-building-image-search-engine-python-opencv/). I got the feature matching with Flann to work and is given an image img_matches with the result drawmatch.

drawMatches( img_1, keypoints_1, img_2, keypoints_2,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

What I want is to take in the two feature vectors vector<keypoint> keypoints_1, keypoints_2; and combine it with good matches vector< DMatch > good_matches; to compute a similarity score in percentage showing how similar the two picture is. Is there a simple similarity or distance metric function that would take in three vectors and help me compute this number?

P.S. My mistake, feature vectors seems to be the descriptors Mat descriptors_1, descriptors_2;.

SURF feature vector keypoints computer similarity percentage

I am following the idea of this point for an image search program (http://www.pyimagesearch.com/2014/12/01/complete-guide-building-image-search-engine-python-opencv/). I got the feature matching with Flann to work and is given an image img_matches with the result drawmatch.

drawMatches( img_1, keypoints_1, img_2, keypoints_2,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

What I want is to take in the two feature vectors vector<keypoint> keypoints_1, keypoints_2; and combine it with good matches vector< DMatch > good_matches; to compute a similarity score in percentage showing how similar the two picture is. Is there a simple similarity or distance metric function that would take in three vectors and help me compute this number?

P.S. My mistake, feature vectors seems seem to be the descriptors Mat descriptors_1, descriptors_2;.

SURF feature vector keypoints computer compute similarity percentage

I am following the idea of this point for an image search program (http://www.pyimagesearch.com/2014/12/01/complete-guide-building-image-search-engine-python-opencv/). I got the feature matching with Flann to work and is given an image img_matches with the result drawmatch.

drawMatches( img_1, keypoints_1, img_2, keypoints_2,
               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

What I want is to take in the two feature vectors vector<keypoint> keypoints_1, keypoints_2; and combine it with good matches vector< DMatch > good_matches; to compute a similarity score in percentage showing how similar the two picture is. Is there a simple similarity or distance metric function that would take in three vectors and help me compute this number?

P.S. My mistake, feature vectors seem to be the descriptors Mat descriptors_1, descriptors_2;.