Match descriptors using nearest neighbor with ratio test

Nearest neighbor matching with ratio test is a simple and effective technique to filter out incorrect matches between feature descriptors. It works by comparing the distances between a descriptor in the first image and its two nearest neighbors in the second image. If the ratio of the distances is below a certain threshold, the match is considered correct.

The ratio test is used to ensure that the nearest neighbor match is significantly closer to the descriptor than the second nearest neighbor. The threshold value for the ratio test is usually set between 0.7 and 0.8, depending on the specific application.

Here is an example of how to perform nearest neighbor matching with ratio test in MATLAB:

% Load feature descriptors for two images
descriptors1 = load('descriptors1.mat');
descriptors2 = load('descriptors2.mat');

% Find nearest neighbors using knnsearch
idx = knnsearch(descriptors2, descriptors1, 'K', 2);

% Apply ratio test
threshold = 0.8;
matches = [];
for i = 1:size(descriptors1, 1)
    ratio = norm(descriptors1(i,:) - descriptors2(idx(i,1),:)) / norm(descriptors1(i,:) - descriptors2(idx(i,2),:));
    if ratio < threshold
        matches = [matches; i, idx(i,1)];
    end
end

% Visualize matches
figure;
showMatchedFeatures(image1, image2, points1(matches(:,1),:), points2(matches(:,2),:));

In this example, the knnsearch function is used to find the two nearest neighbors of each descriptor in the second image for each descriptor in the first image. Then, for each descriptor in the first image, the ratio of the distances between the descriptor and its two nearest neighbors in the second image is computed. If the ratio is below the threshold, the match is considered correct and added to the matches matrix. Finally, the matched keypoints are visualized using the showMatchedFeatures function.