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raarky | 12 years 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&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.

discuss

order

IanCal|12 years 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 [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.

raarky|12 years ago

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.

Peroni|12 years 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|12 years 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