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| 2017-01-23 23:57:42 -0600 | received badge | ● Popular Question (source) |
| 2013-04-19 13:39:27 -0600 | commented answer | How can I use keypoints, descriptors and/or matches to compute similarity scores between images? Thanks for your response, Guanta! I actually tried compareHist already, but it wasn't an option since the images are of different dimensions (mostly varying lengths). I looked at the BoW examples in your link, but they were both in Matlab. Are there any good examples of BoW implemented in OpenCV so that the result is a matching score? I also read something about EMD, which says it can handle images of different dimensions, but I wasn't able to get the code for image signatures working. |
| 2013-04-18 17:06:05 -0600 | asked a question | How can I use keypoints, descriptors and/or matches to compute similarity scores between images? Hi all, So far I have been able to implement a SURF descriptor extractor and a brute force matcher on a collection of grayscale images, much like the example in Chapter 8 of the CV cookbook (link below). But my question is how can I use this information to calculate a similarity score between the images? I know that just number of keypoints is not enough, but can I use them or the matches in an algorithm to find a score? I've seen so much on the web about descriptor extractors and matchers, but absolutely nothing about what to do with the matches, other than just draw them on the image. Surely there must be a good method for computing a similarity score? Thanks for any guidance you can provide. http://code.google.com/p/opencv-cookbook/source/browse/trunk/Chapter%2008/tracking.cpp?r=2 Thanks, Nick |