Object-Recognition using SIFT: Switching the order of images creates two different results I've developed a large test-system which can use every interest-point-detector and -descriptor. I use it for evaluation of the given algorithms with the standford-image-collection of cd-covers. This post will not go any deeper with any source-code. I only found out that switching to images (no referred as firstImg and secondImg) will create to different results. The Ransac-Threshold is used to compute the homography and epsilon is the reprojection-error. For this example I use the Scale-Invariant-Feature-Transform by David Lowe.

First test with the secondImg as the queue-image creates the following results I've written down:


Inliner: "1672"  
Number_of_Keypoints_FST: "368"
Number_of_Keypoints_SND: "1749"
Outlier: "77"
Ransac: "65"
Epsilon: "10"




Now I'm switching the order. The firstImg is the queue.


Inliner: "310"  
Number_of_Keypoints_FST: "1749"
Number_of_Keypoints_SND: "368"
Outlier: "58"
Ransac: "65"
Epsilon: "10"




Questions: I'm confused that switching the order of the images affects the results. Should the matched keypoints be identical for the matched ones when switching the order of the images? Should the inliner be identical?
ARGH. I feel ashamed. How about a cross validation? That's the solution. Now there are almost exact matches ... almost. =) Thank you, Libor. The second comment gave me a hint. =)