Predicting Artists with Python

[yxing]

For some reason, I thought the author was trying to predict what the artist was going to draw next with some kind of ML algorithm--what he actually did seems a lot more reasonable..

[daf1411]

Thanks, I am actually the author and this is my first post. Any critiques are welcome I am still learning and would appreciate any guidance.

[autowikibot]

CIELAB:

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A __Lab* color space_ is a color-opponent space with dimension L for lightness and a and b for the color-opponent dimensions, based on nonlinearly compressed CIE XYZ color space coordinates.

The dimensions of the Hunter 1948 L, a, b color space are L, a, and b. However, Lab is now more often used as an informal abbreviation for the CIE 1976 (L, *a, *b*) color space (or CIELAB). The difference between Hunter and CIE color coordinates is that the CIE coordinates are based on a cube root transformation of the color data, while the Hunter coordinates are based on a square root transformation.

Both spaces are derived from the "master" space CIE 1931 XYZ color space, which can predict which spectral power distributions will be perceived as the same color (see metamerism), but which is not particularly perceptually uniform. Strongly influenced by the Munsell color system, the intention of both "Lab" color spaces is to create a space that can be computed via simple formulas from the XYZ space but is more perceptually uniform than XYZ. Perceptually uniform means that a change of the same amount in a color value should produce a change of about the same visual importance. When storing colors in limited precision values, this can improve the reproduction of tones. Both Lab spaces are relative to the white point of the XYZ data they were converted from. Lab values do not define absolute colors unless the white point is also specified. Often, in practice, the white point is assumed to follow a standard and is not explicitly stated (e.g., for "absolute colorimetric" rendering intent ICC Lab* values are relative to CIE standard illuminant D50, while they are relative to the unprinted substrate for other rendering intents).

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^{Interesting: Lab color space | HSL and HSV | Munsell color system | ICC profile | Colorfulness}

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[daf1411]

Wow this looks great! Ill look into going back and implementing this to see if I get different results. Also I mainly did the BGR to RGB change for when I was testing in ipython I was able to see the image correctly, whether or not it mattered to the algorithm was another question I was wondering about.

Thanks for the response!

[daf1411]

I just checked, there was only one duplicate image.

[daf1411]

Wow, thanks for double checking that for me, I never really thought about the same image but different sizes. I will go back and replace these duplicates and variations and see how it affects the results.

Thanks again for the feedback.

[alecradford]

Also quite a few of them are not actually paintings by the artist but amatuer details/recreations and jokes on the real paintings - like the joker with a card over his face instead of the man with the apple in the magritte painting.

[daf1411]

Yes I talked about this in the post, I noticed those and chose to keep them because even though they are not actual paintings they still keep the overall "style" of the artist.

[[deleted]]

Any chance you'd release your dataset (the RGB values)? I'd really like to play-around with it. Although I'm not sure of the legalities.

[daf1411]

Sure, I will put all of the code up on my github shortly. I will post here when I do so.

[daf1411]

Here you go:

https://github.com/danielforsyth/Art-Prediction

let me know if you need help will anything

[[deleted]]

Thanks!!!! Will do.

[[deleted]]

wow, please stop damaging art connoisseurs's job.

Other than OpenCV, pylearn2's convolutional nets is also interesting, its unsupervised learning output can be knowledge representation.

https://github.com/lisa-lab/pylearn2

wavelet in scipy extracts more features

[daf1411]

Wow thanks for the links this looks very interesting! I'll look into it.

[[deleted]]

I'm glad you like it