This isn’t due to the entropy of the hash but due to the entropy of the source data.
These algos work by limiting the color space of the photo, usually to only black and white (not even grey scale) resizing it to a fraction of its original size and then chopping it into tiles using a fixed size grid.
This increases the chances of collisions greatly because photos with a similar composition are likely to match on a sufficient number of tiles to flag the photo as a match.
This is why the women image was matched to the butterfly image, if you turn the image to B&W resize it to something like 256x256 pixels and divide it into a grid of say 16 tiles all of a sudden a lot of these tiles can match.
These algos work by limiting the color space of the photo, usually to only black and white (not even grey scale) resizing it to a fraction of its original size and then chopping it into tiles using a fixed size grid.
This increases the chances of collisions greatly because photos with a similar composition are likely to match on a sufficient number of tiles to flag the photo as a match.
This is why the women image was matched to the butterfly image, if you turn the image to B&W resize it to something like 256x256 pixels and divide it into a grid of say 16 tiles all of a sudden a lot of these tiles can match.