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.
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.