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I am doing some research on image retrieval. I find your method really innovative. But when I use the model you gave to evaluate on Roxford5k benchmark (Medium level) extracting 1000 keypoints, the conclusion indicators are very low, mAP only 26.7, mp@10 50.14. I also did RANSAC after extracting keypoints.
Is this a reasonable conclusion or I did somethin wrong?
Easier queries seems right, but a little difficult queries with view point change turn out wrong.
thanks~
The text was updated successfully, but these errors were encountered:
lfnet is trained for stereo matching, so we honestly have little idea on how it would perform for image retrieval. A few things that we've noticed is that ratio tests or other heuristics involved in traditional matching could be harmful instead. Thus, a bit of care must be taken when plugging the features into a sophisticated pipeline. Which is true for all local features I guess...
I am doing some research on image retrieval. I find your method really innovative. But when I use the model you gave to evaluate on Roxford5k benchmark (Medium level) extracting 1000 keypoints, the conclusion indicators are very low, mAP only 26.7, mp@10 50.14. I also did RANSAC after extracting keypoints.
Is this a reasonable conclusion or I did somethin wrong?
Easier queries seems right, but a little difficult queries with view point change turn out wrong.
thanks~
The text was updated successfully, but these errors were encountered: