4 ms·

Why not a quick check of the upper-left pixel color, and if that's similar to one of the identified "popular" colors, remove it? I'd suspect most of the source

Why not a quick check of the upper-left pixel color, and if that's similar to one of the identified "popular" colors, remove it? I'd suspect most of the source material would be photographed professionally on a uniform background color. I chose upper-left simply because these sorts of photos would likely would be framed in a way that the background is visible in the upper margins, while possibly cropping the object at the bottom of the frame.

lnanek2 12y ago
Doesn't seem logical to me. What if you have a white coat, with black trim, with white background. Wouldn't your solution incorrectly remove white from the results?
- spdustin 12y ago
  Exceptions to every rule. I would submit that most professionally photographed fashion items would endeavor to include sufficient contrast between the background and the item being photographed.
teh 12y ago
That's a good suggestion! Especially having a "sanity check" against a popular colour which should increase robustness when new images arrive. Robustness is such a tricky subject, especially when the loss, i.e. the cost of returning a wrong image is fairly high. For fashion items the occasional odd colour is OK but accidentally returning naked people can be devastating for a product. I want to stress that this code is massively simplified to avoid obscuring the main plot. There's so much we skipped: the code in the blog post actually computes pretty terrible results compared to an even moderately tuned pipeline :)
- phkahler 12y ago
  >> There's so much we skipped: the code in the blog post actually computes pretty terrible results compared to an even moderately tuned pipeline :) That's fine. I am a day away from starting an image processing app and was thinking of Python and some libraries. This blog confirms that idea and gives the right amount of "starting point" for my effort. My requirements are very different so showing a more specific solution while still interesting is of less universal value. Perhaps a part-II with more application specific methods would be good.
deleted 12y ago
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lnanek2 12y ago

Doesn't seem logical to me. What if you have a white coat, with black trim, with white background. Wouldn't your solution incorrectly remove white from the results?

spdustin 12y ago
Exceptions to every rule. I would submit that most professionally photographed fashion items would endeavor to include sufficient contrast between the background and the item being photographed.

teh 12y ago

That's a good suggestion! Especially having a "sanity check" against a popular colour which should increase robustness when new images arrive. Robustness is such a tricky subject, especially when the loss, i.e. the cost of returning a wrong image is fairly high. For fashion items the occasional odd colour is OK but accidentally returning naked people can be devastating for a product. I want to stress that this code is massively simplified to avoid obscuring the main plot. There's so much we skipped: the code in the blog post actually computes pretty terrible results compared to an even moderately tuned pipeline :)

phkahler 12y ago
>> There's so much we skipped: the code in the blog post actually computes pretty terrible results compared to an even moderately tuned pipeline :) That's fine. I am a day away from starting an image processing app and was thinking of Python and some libraries. This blog confirms that idea and gives the right amount of "starting point" for my effort. My requirements are very different so showing a more specific solution while still interesting is of less universal value. Perhaps a part-II with more application specific methods would be good.

deleted 12y ago

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