131–140 of 155 posts
I have a large collection of images, many being accessible through google image search. I wonder if there could be a way to "index" those images so I can find them back without storing the whole image, using some type of clever image histogram or hashing-kind function. I wonder if that thing already exist, since there are many images, and since most images have a lot of difference in their data, could it be possible…
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That's perceptual hashing. Check out https://www.phash.org/
so will this do something like image recognition? ie does it work as well as surf/sift?
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Color comes from the initial neural network step. Since skin color is relatively predictable from facial features (ex: nose width), it should be able to do reasonably well.
Really? With what accuracy? This is the kind of assumption that will get research groups into very deep water... Just imagine the kind of CCTV usage being discussed elsewhere in this thread. But the neural network happens to have a wrong bias towards skin colour...
One of the best articles I've read on the topic, if you're interested: https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59...
I found the title somewhat misleading. I was expecting some clever application of the nearest-neighbor interpolation. But this seems to involve neural nets and appears far from "simple" to me (I'm not in the image processing field though).
AFAIU it actually seems to be sort of "just" a clever application of the nearest-neighbor interpolation. The CNN is used to come up with the feature space for the pixels (weights of the CNN), and then each pixel is "copy-pasted" from the training set based on the nearest match. It seems that this could be used in theory with any feature descriptors, such as local color histograms, although the results wouldn't probab…
FWIW, what you just described is known as a "Markov process". It is sampling a known conditional probability distribution.
While some interpolation of the data happens because the output represents a mixture of the training images, this is not "interpolation" at the pixel level, it's picking best matches from a search space of image fragments. (And the pixel neighbors are usually synthesized - the best match depends on previous best matches!) This is distinctly different from the kind of nearest neighbor interpolation you'd do when resizing an image.
Note the phrase "nearest neighbor" in this paper has an overloaded double meaning. It is referring both to pixel neighbors and neighbors in the search space of images. The pixel neighbors provide spatial locality within a single image; this is how & why high frequencies are generated from the training set. Nearest neighbor is also referring to the neighborhood matches in the search space, the K nearest neighbors of a given pixel neighborhood are used to generate the next K pixel outputs in the synthesis phase.
I used to roll my eyes at crime television shows, whenever they said "Enhance" for a low quality image. Now it seems the possibility of that becoming realistic are increasing with a steady clip, based on this paper and other enhancement techniques I've seen posted here.
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I don't think this method has anything to do with Markov Chains. The spatial structure isn't explicitly used at all, and the interpolation/regression is quite a vanilla nearest neighbor with some performance tricks. Well, of course almost anything can be interpreted as a Markov process, but I don't think it's a very useful abstraction here.
> I don't think this method has anything to do with Markov Chains. Oh, it absolutely does. I think it's fair to say that Efros launched the field of nearest neighbor texture synthesis, and his abstract states: "The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors sy…
And I didn't mean that Markov processes are abstract in any "distant" sense, but that they are an abstraction, ie a "perspective" from which to approach and formulate the problem.
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> It's similar to that guy who used Photoshop's swirl effect to hide his face, not knowing that the effect is deterministic, and can easily be undone. The effect does not only need to be deterministic, but also invertible. A low-res image has multiple "inverses" (yikes), supposedly each with an associated probability (if you would model it that way). So it would be more honest if the algorithm shows them all.
It's what they call an https://en.wikipedia.org/wiki/Inverse_problem
I don't understand how the edges-to-faces can possibly work. The inputs seem to be black & white, and yet the output pictures have light skin tones. How can their algorithm work out the skin tone from a colourless image. Perhaps their training data only had white people in it?
You never saw edges2cats I take it? https://affinelayer.com/pixsrv/ > I don't understand how the edges-to-faces can possibly work. The inputs seem to be black & white, and yet the output pictures have light skin tones. The step you're missing is that an edge detector is run on the entire database of training images to produce a database of edge images. The input edge image is run against that corpus of edge images in…
But shoes and cats are one thing; reconstructing people's faces is another. I know the paper & the authors are demonstrating a technology here, rather than directly saying "you can use this technology for purpose X", but the discussion in these comments has jumped straight into enhancing images and improving existing pictures/video. But there is a very big line between 'reconstituting' or 'reconstructing' an image and 'synthesising' or 'creating' an image, and it appears many people are blurring the two together. Again, in the authors' defence, they are clear that they talk about the 'synthesis' of images, but the difference is critical.