10 ms·

Color Detection

119 points by Peroni 13y ago

tsumnia 13y ago
Really cool techniques (this is the background removal one). I'm curious on how well this would work on hair color detection. My thoughts are that you could run face detection, and since detection almost always (anecdotal) find just the face, not the outline of the face, you could just grab around the face for hair. Does someone at Lyst have a background in image manipulation? If so, I can't wait to see what else you guys are working on.
- ejlb 13y ago
  Most of the guys here have more experience with text processing but we are expanding into image processing. I think we could definitely detect hair colour but it isn't that useful for Lyst. Gender identification on the other hand...(watch this space)
  - tsumnia 13y ago
    I'll be curious about the gender classifier, mostly because I'm working on a statistical aging model for different gender/ethnicities. Keep on posting!
  - adyus 13y ago
    That's a very sensitive area to be heading into. Any assumptions you might try to make will find themselves with counter-examples quite often. A better direction to move in might be to minimize gender differences as much as possible (although that's hard for marketing to stomach). I mean, would one try not to recommend pants for girls or skirts for boys (kilts)? It's tricky...
raarky 13y ago
Nice. 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&ratios=74,26 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 & 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.
- Peroni 13y ago
  For something that's so early stage, that's a really clever tool you're building. Drop me an email (in my profile) if you ever fancy popping in for a chat with the team here.
- ejlb 13y ago
  Wow, looks awesome so far. At the moment we aren't dealing with skin tones. Mostly it isn't a problem but there are some cases (such as jewellery or swimwear) where we have to use humans
- jpatel3 13y ago
  This one is interesting and kind of on same path if you haven't come across yet - http://plumperfect.com/ http://plumperfect.com/
- IanCal 13y ago
  > 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 detection & a few other bits should give you pretty reliable skin blocks. I've used it a few times before. Here's a presentation I did some years ago that I apparently still have on my desktop: http://files.figshare.com/1409002/1.pdf http://files.figshare.com/1409002/1.pdf [1] [0] http://www-cs-students.stanford.edu/~robles/ee368/skincolor.html http://www-cs-students.stanford.edu/~robles/ee368/skincolor.... [1] Calvert, Ian (2014): Finger pointing detection. figshare. http://dx.doi.org/10.6084/m9.figshare.953171 http://dx.doi.org/10.6084/m9.figshare.953171 EDIT - I'm sure there are many good approaches for this, and many fancy ones. This is very simple and was researched/written purely for fun in a couple of weeks. EDIT 2 - The final slide shows the more interesting part, where you use edge detectors to guide your estimation of what is inside or outside a shape. That plus an adaptive threshold (designed to stop if the number of pixels included jumped rapidly) got some good results, but I've not got the code any more.
  - 13y ago
netw1z 13y ago
haha image is labeled pretty white girl. http://developers.lyst.com/images/color_detection/pretty-white-girl.png http://developers.lyst.com/images/color_detection/pretty-whi... racists! saved for later.
- leeoniya 13y ago
  the only thing that's white is her sweater
Bjoern 13y ago
Nice post! Made me think we should write something similar for our approach, which is a bit more complex though leveraging semi-supervised learning, dynamic estimations and normalized color calculations with statistical bias. Here is some simple demo if you are interested, where we translated it back also into color names to allow text search for estimated parent and real color. http://text.clothesnetwork.com/ http://text.clothesnetwork.com/ http://text.clothesnetwork.com/tags http://text.clothesnetwork.com/tags
vision_cat 13y ago
Nice post. I'm a researcher in computer vision, so I'm glad to see that people find these problems interesting. It's often easy to underestimate how difficult vision problems are, since they seem so natural to us. I think the recent posts by lyst (this color naming post, and the previous background subtraction post) are great introductions to problems in computer vision. However, there is a lot more sophisticated work out there, and the techniques used in these posts are decades old (and contain some errors). If you liked this post, you should check out this more recent demo http://clothingparsing.com/ http://clothingparsing.com/ If you have a computer vision problem in mind, you can gauge the state-of-the-art, by searching recent papers on Google Scholar. Even if you're unfamiliar with the jargon, the introduction and conclusion of a paper can give you an intuition about the problem being solved and the steps the authors are proposing. If you want to learn more about computer vision and its details, I highly recommend checking out online materials such as http://cs.brown.edu/courses/cs143/ http://cs.brown.edu/courses/cs143/ http://www.cs.cornell.edu/courses/cs4670/2013fa/lectures/lectures.html http://www.cs.cornell.edu/courses/cs4670/2013fa/lectures/lec... http://szeliski.org/Book/ http://szeliski.org/Book/
- Bjoern 13y ago
  Nice! Clothingparsing very cool.
- ejlb 13y ago
  Thanks for all the great resources. I think it must be obvious that we aren't image processing researchers! My background is combinatorial graph theory. But these HN comments have given us so much valuable material for improvements.
strebler 13y ago
Color 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 http://www.pcsso.com/demo/macys.htm
- Bjoern 13y ago
  Yeah, totally agree on that. We noticed this as well when approaching it naively via simple clustering then going through different color spaces and methods until finally getting some decent results.
- jacobolus 13y ago
  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.
  - strebler 13y ago
    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.
TheZenPsycho 13y ago
"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 are now available in the colormath package and can be used in the following manner." Uhm.. wouldn't it have been easier to just run a voronoi algorithm on the data set in the labspace? then you have a lookup table/cube. For a paltry 6mb of space, lookup becomes instantaneous. It's what voronoi is for.
- jacobolus 13y ago
  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.
kimonos 13y ago
Very interesting! Thanks for sharing!
jacobolus 13y ago
I’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.

119 points by Peroni 13y ago

Really cool techniques (this is the background removal one). I'm curious on how well this would work on hair color detection. My thoughts are that you could run face detection, and since detection almost always (anecdotal) find just the face, not the outline of the face, you could just grab around the face for hair. Does someone at Lyst have a background in image manipulation? If so, I can't wait to see what else you guys are working on.

ejlb 13y ago
Most of the guys here have more experience with text processing but we are expanding into image processing. I think we could definitely detect hair colour but it isn't that useful for Lyst. Gender identification on the other hand...(watch this space)
- tsumnia 13y ago
  I'll be curious about the gender classifier, mostly because I'm working on a statistical aging model for different gender/ethnicities. Keep on posting!
- adyus 13y ago
  That's a very sensitive area to be heading into. Any assumptions you might try to make will find themselves with counter-examples quite often. A better direction to move in might be to minimize gender differences as much as possible (although that's hard for marketing to stomach). I mean, would one try not to recommend pants for girls or skirts for boys (kilts)? It's tricky...

raarky 13y ago

Nice. 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&ratios=74,26 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 & 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.

Peroni 13y ago
For something that's so early stage, that's a really clever tool you're building. Drop me an email (in my profile) if you ever fancy popping in for a chat with the team here.
ejlb 13y ago
Wow, looks awesome so far. At the moment we aren't dealing with skin tones. Mostly it isn't a problem but there are some cases (such as jewellery or swimwear) where we have to use humans
jpatel3 13y ago
This one is interesting and kind of on same path if you haven't come across yet - http://plumperfect.com/ http://plumperfect.com/
IanCal 13y ago
> 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 detection & a few other bits should give you pretty reliable skin blocks. I've used it a few times before. Here's a presentation I did some years ago that I apparently still have on my desktop: http://files.figshare.com/1409002/1.pdf http://files.figshare.com/1409002/1.pdf [1] [0] http://www-cs-students.stanford.edu/~robles/ee368/skincolor.html http://www-cs-students.stanford.edu/~robles/ee368/skincolor.... [1] Calvert, Ian (2014): Finger pointing detection. figshare. http://dx.doi.org/10.6084/m9.figshare.953171 http://dx.doi.org/10.6084/m9.figshare.953171 EDIT - I'm sure there are many good approaches for this, and many fancy ones. This is very simple and was researched/written purely for fun in a couple of weeks. EDIT 2 - The final slide shows the more interesting part, where you use edge detectors to guide your estimation of what is inside or outside a shape. That plus an adaptive threshold (designed to stop if the number of pixels included jumped rapidly) got some good results, but I've not got the code any more.
- 13y ago

netw1z 13y ago

haha image is labeled pretty white girl. http://developers.lyst.com/images/color_detection/pretty-white-girl.png http://developers.lyst.com/images/color_detection/pretty-whi... racists! saved for later.

leeoniya 13y ago
the only thing that's white is her sweater

Bjoern 13y ago

Nice post! Made me think we should write something similar for our approach, which is a bit more complex though leveraging semi-supervised learning, dynamic estimations and normalized color calculations with statistical bias. Here is some simple demo if you are interested, where we translated it back also into color names to allow text search for estimated parent and real color. http://text.clothesnetwork.com/ http://text.clothesnetwork.com/ http://text.clothesnetwork.com/tags http://text.clothesnetwork.com/tags

vision_cat 13y ago

Nice post. I'm a researcher in computer vision, so I'm glad to see that people find these problems interesting. It's often easy to underestimate how difficult vision problems are, since they seem so natural to us. I think the recent posts by lyst (this color naming post, and the previous background subtraction post) are great introductions to problems in computer vision. However, there is a lot more sophisticated work out there, and the techniques used in these posts are decades old (and contain some errors). If you liked this post, you should check out this more recent demo http://clothingparsing.com/ http://clothingparsing.com/ If you have a computer vision problem in mind, you can gauge the state-of-the-art, by searching recent papers on Google Scholar. Even if you're unfamiliar with the jargon, the introduction and conclusion of a paper can give you an intuition about the problem being solved and the steps the authors are proposing. If you want to learn more about computer vision and its details, I highly recommend checking out online materials such as http://cs.brown.edu/courses/cs143/ http://cs.brown.edu/courses/cs143/ http://www.cs.cornell.edu/courses/cs4670/2013fa/lectures/lectures.html http://www.cs.cornell.edu/courses/cs4670/2013fa/lectures/lec... http://szeliski.org/Book/ http://szeliski.org/Book/

Bjoern 13y ago
Nice! Clothingparsing very cool.
ejlb 13y ago
Thanks for all the great resources. I think it must be obvious that we aren't image processing researchers! My background is combinatorial graph theory. But these HN comments have given us so much valuable material for improvements.

strebler 13y ago

Color 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 http://www.pcsso.com/demo/macys.htm

Bjoern 13y ago
Yeah, totally agree on that. We noticed this as well when approaching it naively via simple clustering then going through different color spaces and methods until finally getting some decent results.
jacobolus 13y ago
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.
- strebler 13y ago
  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.

TheZenPsycho 13y ago

"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 are now available in the colormath package and can be used in the following manner." Uhm.. wouldn't it have been easier to just run a voronoi algorithm on the data set in the labspace? then you have a lookup table/cube. For a paltry 6mb of space, lookup becomes instantaneous. It's what voronoi is for.

jacobolus 13y ago
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.

kimonos 13y ago

Very interesting! Thanks for sharing!

jacobolus 13y ago

I’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.