3 ms·
It's interesting to note that conceptually stuff like pHash (which is very useful) is similar to dimensionality reduction as we see it in machine-learning/data-
by inglor 5y ago
It's interesting to note that conceptually stuff like pHash (which is very useful) is similar to dimensionality reduction as we see it in machine-learning/data-science scenarios.
- igorkraw 5y agoIt's not just similar, learning a classifier is 100% identical to finding a perceptual hash onto your classes that aligns with human sensibilities
- edge17 5y agoKind of. The only point of phash is to make comparing images fast and cheap... meaning from a practical standpoint there isn't a ton of useful information in the hash. All you really care about is the final hamming distance between two hashes. I think classifiers tend to encode actual interesting information.
- robbedpeter 5y agoThey can, but neural architectures range in granularity from basic autoencoders with softmax which can be equivalent to a hash function, to convolutional networks which explicitly encode fine detail. Neural networks are a great tool that when used well can operate with explicit functions like phash- they don't have to be a black box mystery. Pick the right architecture and tolerance and you might eliminate a lot of development, maintenance, and troubleshooting.