> 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 :)
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/