Setting parameters for BRISK in OpenCV

Question

I'm trying to use BRISK implementation of OpenCV (for C++) in order to check in a photo if an image (or a part of an image) is included in. For example, I take a photo, and I try to match it with a set of images in database, and I would like to select the best corresponding image (or an error message if none of all the images is good enough).

So, I'm just testing OpenCV for the moment. I've simply taken the sample included in the framework (matching_to_many_images), and change the detector and descriptor from SURF to BRISK.

However, I have weird results. These are the results of matching (BruteForce Hamming):

In the first one, the scenes are entirely different, but there are a lot of matches! In the second one, the scenes are pretty similar, but some matches are wrong.

I think this is a parameters issue- because on demo videos of BRISK, the results are significant.

Answer 1

Have you seen the OpenCV documentation for BRISK? I'm not sure what parameters you're using now, but you can specify the threshold and octaves, as well as the pattern. Documentation at http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html#brisk

Also you could try a different feature matching algorithm, although it appears that in the BRISK paper they also used hamming distance

Lastly, it's not too unexpected to have erroneous feature matches; try out different scenes as well as different feature parameters and see how your results are

Answer 2

There are commonly many incorrect initial matches when doing feature-feature matching using SIFT, SURF, BRISK, or any other local descriptor.

Many of these initial matches will be incorrect due to ambiguous features or features that arise from background clutter. [From Distinctive Image Features from Scale Invariant Keypoints]

The next step is to select only a subset of those matches that all agree on a common transformation between the two images. This is explained in sections 7.3 and 7.4 of Distinctive Image Features from Scale Invariant Keypoints.

Answer 3

The OpenCV Tutorial gives an excellent example of how to extract features and calculate a homography (a transformation that tells you how to to transform each point from one image to the other one).

You can replace the feature-detector/descriptor with any other one, which will result in different robustness to certain transformations like rotation, scaling or errors like blur or lumination change. The basic implementation of BRISK already has meaningful parameters defined.

Last but not least, if you try to match two completely different images, what would you expect as a result? The algorithm will try to find similarities, and therefore always calculate a result, even if it is non-sense and the scores are very low. Just keep in mind: Garbage in -> Garbage out.