Color Detection

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.

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&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.

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

> 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 [1]

[0] 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

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.

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/tags