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 | 1 | initial version |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring )
| | 2 | No.2 Revision |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
(trying to cheat by converting it to float does not help, since it gets the distance between descriptors entirely wrong)
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring )
| | 3 | No.3 Revision |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
(trying to cheat by converting it to float does not help, since it gets the distance between descriptors entirely wrong)
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring bitstring. if you wanted to cluster bitstrings, it would need something like "kmedian", which opencv does not supply as of now. )
| | 4 | No.4 Revision |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
(trying to cheat by converting it to float does not help, since it gets the distance between descriptors entirely wrong)
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring. if you wanted to cluster bitstrings, it would need something like "kmedian", "kmedian" or "kmajority", which opencv does not supply as of now. )
| | 5 | No.5 Revision |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
(trying to cheat by converting it to float does not help, since it gets the distance between descriptors entirely wrong)wrong, that's what you're seing here.)
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring. if you wanted to cluster bitstrings, it would need something like "kmedian" or "kmajority", which opencv does not supply as of now. )
| | 6 | No.6 Revision |
BowKmeansClustering requires float descriptors, while FREAK is a binary one, and cannot be used here. (you can only use SIFT, SURF or KAZE with BOW).
(trying to cheat by converting it the FREAK descriptor to float does not help, since it gets the distance between descriptors entirely wrong, that's what you're seing here.)
again, kmeans uses L2 distance, while you have to use hamming distance for binary descriptors. (also the "mean" in kmeans does not make sense with a bitstring. if you wanted to cluster bitstrings, it would need something like "kmedian" or "kmajority", which opencv does not supply as of now. )
