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Old April 12th, 2019, 10:11 AM   #1
Paul McGhee
Join Date: Sep 2011
Location: Cambridge England
Posts: 9
Default Pattern Recognition for Rugs

I live in Cambridge (UK) where a significant percentage of the population are mathematicians and software developers, many of them obsessed with what they call “pattern recognition”. Showing a computer how to look for patterns in large collections of data usually involves showing it a “training” data set – a lot of known examples of something which it "learns" to recognise – then letting it loose on a random data set and asking what it thinks about a new item.

I’m not a mathematician or a software developer, but my neighbours are, so chatting to them got me thinking. If you wanted to teach a computer to identify where a particular rug comes from, just from a digital image, (so no info about knotting, handle and so forth) how would you do it? And has anyone already done it? My mind seems to work like this:

1) My first impressions of a new rug are always of the overall colour balance – Baluchis will mostly be in four basic gloomy colours, Kazaks will mostly be bright and cheery, Nain’s will mostly be very light.

2) Then I look at the overall shape or aspect ratio. Is it rectangular? Is the rectangle long and thin or fat and stubby? Is it squarish? Then it’s probably a bag or a sofreh.

3) Then I look at the layout. The relationship between the borders and the field seems to be a key factor here, then the contents of the field. Are there medallions? All-over patterns? Niches which suggest prayer rug?

4) At some stage I might spot a distinctive pattern or colour which is a strong indicator of a singular origin, like some Gendge stripes a Seistani green.

5) I could then, like a well-trained computer, say “this rug probably comes from here but I’m not sure”.

I’d be interested to know how other people recognise rugs. I’m sure it would be relatively easy for a computer to distinguish generic colour palettes and to look out for distinctive spikes of a particular hue. It could measure overall aspect ratios and, with a bit more effort, work out what was a border and what was a field. Other stuff might be harder.

As soon as you start thinking about pattern recognition in rugs, you realise that it is a non-trivial problem. For a start, a large percentage of pictures of rugs on the Internet are trapezoidal and you might need to transform them into rectangles before you start doing anything else. They come in various degrees of fuzziness and there aren’t that many really high-definition images. etc. etc.

Then you think about the big data sets. Much of the classification meta-data you might use would be questionable. No-one would set up a field where “Bokhara” was a definition any more, but what about Anatolian or Baluch? Would there need to be standard terms agreed between experts?

Then you think “Would it be good if there was an app that you could feed an image into and get a definitive judgement from an expert system or is it more fun to post it on Turkotek and get human responses?”

I have a feeling that rug recognition is one area where human experts will still have the edge on computers for quite a while. Any thoughts?


Last edited by Paul McGhee; April 12th, 2019 at 11:10 AM.
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