What I observed people do was almost magic at ImageClef. I'll look through
your training set, I was however thinking of using existing galleries as a
means to identify content.

  -- とある白い猫  (To Aru Shiroi Neko)


On Mon, Sep 26, 2011 at 18:43, Paul Houle <[email protected]> wrote:

> **
> On 9/20/2011 9:36 AM, とある白い猫 wrote:
>
> The conference has no such tool yet, at least nothing we can
> use tomorrow but they are able to pretty accurately on what the images are.
> I am going to try to propose if they would be interested in providing
> commons with such a service, The website relevant is
> http://www.imageclef.org/2011/Wikipedia for commons but
> http://www.imageclef.org/2011/Plants is also interesting (even though it
> had nothing to do with commons so far. It could ver well be used for commons
> and wikispecies alike.
>
>   -- とある白い猫  (To Aru Shiroi Neko)
>
>
>         I've made some attempt to map images on Wikimedia commons to
> distinct concepts from DBpedia,  see
>
> http://ookaboo.com/
>
>       This could be useful for forming a training set,  but I haven't yet
> got around to releasing a public dump of the data.  I have about 1 million
> things classified and could certainly extend the strategies used to get
> more.
>
>       Unless there's been a really unprecedented breakthrough,  I'd think
> that the application of machine vision to Wikimedia faces the problem of
> getting enough training data.  If you had thousands or tens of thousands of
> photos that were labeled 'cat' or 'not cat',  or 'member of plant species X'
> or 'not member of plant species X',  you can train a classifier to make the
> distinction.  However,  if you've got two or three bad photos of a
> particular plant (which is what you have most of the times in Commons) you
> don't have enough training data to generalize.
>
>       If you've got a specific mission,  say genitals recognition, I think
> you can make progress,  but to attack the general problem you need to go big
> with your training sets.
>
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>
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