Very interesting! Thanks for sharing!
Color Detection
21–27 of 27 posts
Re: Color Detection
#22I’d recommend using the ISCC-NBS system of color designation in cases like this. You’ll have to convert your colors to Munsell color space, and then use a lookup table to get the associated name. But it’s a pretty reasonable system.
Ideally you could get some kind of actual color measurements from clothes directly, instead of relying on (often very inaccurate) digital images, but maybe the’s too much to hope for.
Re: Color Detection
#23"To speed up the color difference calculations we decided to vectorised deltaE. The standard deltaE functions are already implemented in the colormath python package. We ported these to numpy and added a function to take a numpy array of Lab coordinates. When used with large data-sets the vectorised implementation is 25-180 times faster depending on which distance function is used. The vectorised delta E functions ar…
Even better would be to take the full data set of all the survey responses, and use it to generate the partition of CIELAB space, instead of just using the single set of coordinates per name that Randall generated from those. But it would be more work.
Edit: do note they’re using a more complicated distance definition such that a straight Voronoi diagram in CIELAB space is not quite the same as their result. But the difference is so slight as to not matter, so your suggested solution would be substantially better than theirs.
Re: Color Detection
#24Color is actually much more difficult than most people realize! Even CIE2000 color distance still has fundamental "perceptual distance" problems, especially comparing non-saturated with saturated colors (although that's a blog post in its own). Here's our engine linking "real world" photos into the Macys catalogue (based on color, shape, texture): http://www.pcsso.com/demo/macys.htm
It is impossible to create a three dimensional color space in which Euclidean distance corresponds directly to perceived color difference. There are dozens of papers on this topic, for anyone who wants some fun evening reading.
Re: Color Detection
#25Nice. I'm building something similar in my spare time. It performs multi-color image searching on street wear. You can select a bunch of colors and adjust the ratios. Here's a very early in-development version. http://www.inthatstyle.com/womens?colors=73a1d3,e84b34&ratio... (I'm a little worried about posting that on HN since it's unoptimized and will probably crash.) I'm currently working on skin detection & exclusi…
> I'm currently working on skin detection & exclusion during the color detection phase and am looking at using basic machine learning techniques. The key challenge I'm facing is differences in skin tones. Try looking at the chromatic colour rather than the RGB values. You can get extremely far with just this, most skin colours fall into one of two peaks [0], no machine learning needed. Once you've got this, edge dete…
awesome stuff :D Thanks for this. Will definitely look into some of that in more detail.
Another tricky part of skin detection is false positives. ie, what if the actual product is that color?
Some things I've noticed and will be taking into account are: Skin areas tend to clump around the same locations in photos. The product is usually the focus and skin is near the edges. Product types also tend to share similar photo layouts. So with that, skin color in those zones score higher.
Re: Color Detection
#26Earlier quoted context omitted.
> I'm currently working on skin detection & exclusion during the color detection phase and am looking at using basic machine learning techniques. The key challenge I'm facing is differences in skin tones. Try looking at the chromatic colour rather than the RGB values. You can get extremely far with just this, most skin colours fall into one of two peaks [0], no machine learning needed. Once you've got this, edge dete…
awesome stuff :D Thanks for this. Will definitely look into some of that in more detail. Another tricky part of skin detection is false positives. ie, what if the actual product is that color? Some things I've noticed and will be taking into account are: Skin areas tend to clump around the same locations in photos. The product is usually the focus and skin is near the edges. Product types also tend to share similar p…
> awesome stuff :D Thanks for this. Will definitely look into some of that in more detail.
No worries, hope it helps, it was just a quick project back in the day at uni that ended up working a lot better than I expected.
Give me a shout if you want any work done on it (my email address is in my profile).
> Some things I've noticed and will be taking into account are: Skin areas tend to clump around the same locations in photos. The product is usually the focus and skin is near the edges. Product types also tend to share similar photo layouts. So with that, skin color in those zones score higher.
This kind of thing will really help you, small bits of knowledge about the specifics drastically simplify the problem. For example, you can estimate the skin tone by roughly segmenting the image into possible skin/not skin with the approach above, then look at segments which are more likely to be skin because of their positioning you can narrow your accepted parameters and hopefully help distinguish between the two.
Identification of unusual edges/shapes can help too, to classify regions as skin/not skin.
Beyond that, starting to look at estimations of pose to help guess the underlying shape (since you know it's on humans you can make a lot of assumptions).
Also, since you're detecting colours, mistaking very similarly coloured skin as the product wouldn't change your results much :)
visual_cat posted a really nice site with some of the state of the art: http://clothingparsing.com/
Re: Color Detection
#27Color is actually much more difficult than most people realize! Even CIE2000 color distance still has fundamental "perceptual distance" problems, especially comparing non-saturated with saturated colors (although that's a blog post in its own). Here's our engine linking "real world" photos into the Macys catalogue (based on color, shape, texture): http://www.pcsso.com/demo/macys.htm
It is impossible to create a three dimensional color space in which Euclidean distance corresponds directly to perceived color difference. There are dozens of papers on this topic, for anyone who wants some fun evening reading.
Yes, absolutely, which (I'm assuming) is why CIE2K difference is not-quite-euclidean. My opinion is the we still need to find a better difference metric, rather than improving the color space.
On that front, it's amazing to see how much of an effect "switching color spaces" can have on many algorithms.