A more tactful question might be, "How is it different from existing implementations?" And then don't ask "Am I missing something?"

Best practices for engineering is always to see what else is out there, so to assume the author hasn't conducted a review of the field might be read as rudeness or snark.

Many developers, even when they do compare and contrast their own work as they develop it, don't write up the results or list their peer projects, so it's otherwise not an unfair question to ask.

But I actually want to know what I'm missing-- what's interesting about this "closed source but available for academic purposes" application which appears to simply be a re-implementation of existing work?

I'm trying to give benefit of the doubt here actually.

It's using a very similar technique to pHash, actually. DCT-based hash and all. It's not brand new, but perhaps a variation on a theme.

I've actually been working in this domain for the last week or so and DCT-based hashes are quite accurate, but slow as hell. Average hashes (aHash) or Delta Hashes (dHash) are much faster and rather good at weeding out large numbers of images. A mix of ideas is usually a good idea.

Yup, it's been shown that the accuracy of a set of poor classifiers together can be greater than a known 'good' classifier.

The implication is that there's no single 'perfect' classifier for a problem, engineers have used this notion for many years having multiple systems vote for fail-proof operations.

The article directly describes the phash algorithm presented in the Hacker Factor post, while your second link is an implementation of the ahash algorithm in the same post.

libpHash's implementation is slightly more sophisticated (and slower) as it adds a box filter over the image before downscaling and uses the median of the AC coefficients rather than mean for computing the hash bits. It also offers a few alternative hashing methods.