Mike,

 

It's not all "geometric".  Patterns need not be defined by vector' lines, or
only magnitudes of image properties. The same recognition mechanisms in the
brain are emulatable by mathematical, indexable, categorizable, recognizable
and systematic, engineered processes. Even images of Madonna- which it's
nice to whip out more complex(complicated) examples to entice refutement -
you should start off with simpler and then move to complex a.k.a.
engineering verses philosophy. But building up a pattern recognizer is not
something that is formidable, it's just work that needs to be done. I don't
see problems here even with complex imagery, video it's just resources. Sure
some algorithms need to be refined and an AGI algorithm verses standard
pattern recognition hardcoded- the same algorithms should be applicable to
visual, audial, language - over a diverse set of I/O streams.

 

john

 

From: Mike Tintner [mailto:[EMAIL PROTECTED] 
Sent: Tuesday, May 13, 2008 4:01 PM
To: agi@v2.listbox.com
Subject: Re: [agi] Re: pattern definition

 

Joe,

 

Thanks for reply - yes, I thought you meant something like this, but it's
good to have it spelled out.

 

I think you're making what seems to me to be a v. common mistake among
AGI-ers. Yes, you can reduce any image whatsoever on a computer screen, to
some set of mathemetical formulae/properties. You can reduce it to so many
lines, points, triangles, fractals etc. etc

 

But that's not the problem.

 

The problem is: how do you do that *systematically* for a SET of images (not
just one)? How can you guarantee (or come anywhere remotely close) that your
system of GEOMETRIC FORM analysis will be able to recognize the same OBJECT
FORMS in many different images?  -   that by breaking complex images down
into all those lines, points etc in whatever way you choose,  you will be
able to recognize, say, the faces, noses, mouths, necks etc in several,
different images? Or the plastic bags in them?

 

To focus the problem - in admittedly a v. difficult form (but hopefully it
will help you focus better) -  how will your *geometric* system recognize
the faces and their parts in this set of images, as humans can:

 

http://cr.middlebury.edu/public/spanish/sp371/images/esperpento/goya_viejos.
jpg
http://www.thebestlinks.com/images/2/2f/El_Greco.jpg
http://www.nzine.co.nz/images/articles/picasso_lg.jpg
http://www.roussard.com/media/oeuvres/modigliani/lithos/modiglianiIMGP6719.j
pg
http://www.gerard-schurmann.com/bacon.jpg
http://aphrabehn.files.wordpress.com/2007/03/scarfe1.jpg
http://www.oppdalfilmklubb.no/img/the-wall.jpg
http://www.frederickwildman.com/wildmansite/wmphp/images/hugel/10large.jpg
http://internat.martinique.free.fr/images/le_sommeil-salvador_dali.jpg

 

(Note that even a set of ordinarily photographed faces in different
positions will still present all kinds of recognition problems).

 

How IOW do you equate an OBJECT FORM like that of face/ nose/ mouth/ chair/
tree/ oak/ handbag etc. etc. with GEOMETRIC FORMS?

 

I am pretty sure that no such equation is possible, period -  given that
objects can take a  vast if not infinite range of forms from different
POV's.and in different positions. 

 

And that surely is what the history of failures in visual object recognition
tells us. (What BTW is *your* explanation of that history of failure? It is
rather surprising (no?) that so many AGI-ers can state that images can
definitely be analysed geometrically, given the field's striking lack of
success here. Surely a certain amount of questioning and
soul-or-some-part-of-brain-searching is in order here).

 

I think it's worth thrashing this subject out, because it keeps cropping up
here and elsewhere and is so important - and you seem like a reasonable guy,
so maybe we (& anyone else) can do that. I think my distinction between
geometric form and object form is v. helpful for discussions here, & it may
not be at all new, but it doesn't seem to be commonplace.

 

P.S. Yes, bucket is a simple object - and it's conceivable that a lot of
people might come up with similar mental visualisations of the concept - but
even then you might be surprised - and McLuhan's point  was re WORD
descriptions of buckets and other objects. If you think you can describe it
or almost any other object verbally, be my guest :). 

 

Even recognising the buckets in different images - (and therefore developing
a viable equation of bucket with geometric forms) - strikes me as no simple
task for a computer:

 

http://classroomclipart.com/images/gallery/New/Clipart3/paint_brush.jpg

http://www.craftamerica.com/images/products/6500_75_rusty_tin_bucket.jpg

http://christopher-pelley.abbozzogallery.com/images/red%20bucket.jpg

http://www1.istockphoto.com/file_thumbview_approve/487639/2/istockphoto_4876
39_bucket_and_spade.jpg

http://z.about.com/d/hotels/1/0/l/G/bucket.jpg

http://www.bobjonespaintings.com/large%20images/bucket.jpg

http://www.jenklairkids.com/Eshop/products/girl_dog_bucket.jpg

http://annievic.accountsupport.com/images/beachrosebucket.jpg

http://www.entretienardiz.com/images/cleaning_bucket.jpg

 

Well, I figured the point of the question would be pretty clear seeing as
you were claiming a logical/mathematical description of images would be
inadequate, but I don't think that could be further from the truth...

You could recreate a large amount of detail in an image using mathematics
and it would be a great deal more compact of a description than a bitmap
representation. AND, you could store the mathematical properties of the
image in a DB of some sort to find similar shapes within a large body of
images.

When you remember scenes from years ago, it is unlikely that you remember
which way the grain of the wood was facing, or how many scratches were on
the left side of a corner table's handle... so why not represent them in a
similar manner as vector images?

Using a mathematical description would allow for a more compact, searchable,
and inference-available representation.

To me, keeping a little box of mathematical expressions to describe an image
is a far better choice of both memory and potential processing power than to
lug around a bitmap or any sort, unless you want to store the image in case
you find another way to interpret it.

Though humans don't remember everyday scenes through mathematical
description, we do prune out the irrelevant stuff by only remembering
general shapes, common textures, and orientations (unless we make it a
priority to remember specific features).

Mathematical equations and logical statements might not tell YOU a lot about
an image, but if someone were to sit down and carefully describe an image
using logical statements, equalities, and mathematical expressions, and if
someone were to sit down and carefully read these... with time the image
would emerge, though we humans aren't suited for this task, a machine will
be more than capable, and it would be a much wiser use of resources.

- Joe

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