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 <paul(a)ontology2.com> 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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