Can hog feature descriptor be used to create an efficient hash?
KilianB opened this issue · 1 comments
KilianB commented
The hog feature descriptor pools gradient vectors based on their unsigned direction. It is successfully used in pedestrian detection.
So far a derived descriptor based on https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf is implemented but we are still left with way to many numbers to encode them into a short hash.
| Images to compare | normalized hamming distance |
|---|---|
| Similar images | |
| HQ - HQ: | 0.000 |
| HQ - LQ: | 0.292 |
| HQ - Copy: | 0.119 |
| HQ - Thumb | 0.422 |
| LQ - Copy | 0.319 |
| LQ - Thumb | 0.396 |
| Copy - Thumb | 0.424 |
| Unlike Images | |
| HQ - Ballon: | 0.496 |
| HQ - Lena: | 0.491 |
| LQ - Ballon | 0.484 |
| LQ - Lena | 0.483 |
| Copy - Ballon | 0.491 |
| Copy - Lena | 0.493 |
| Thumb - Ballon | 0.506 |
| Thumb - Lena | 0.481 |
While similar images can be differentiated from unlike images it's rather expensive, the hash is long. A todo on figuring out if the approach can be tweaked to produce usable results.