Given an image of many items, with all of its bounding box known in pixel coordinates.
I am trying to extract a region (surrounding) around each of the items, calculate its descriptors and features using AKAZE, to do comparison with one another.
However I realised that this might be too slow, since it involves:
1) cropping each and every single item to generate many images then,
2) detecting and computing on each image to generate the keypoints and descriptors.
Alternatively, to speed things up, I was thinking of:
1) Resizing the entire image, then perform the detecting and computing of keypoints once.
2) Then to obtain the keypoint of a particular object, we simply retrieve the set of precalculated keypoints corresponding to the objects location.
My question is this method functionally sound, and that if there are any consequences to this?