C
Casimir
Still no luck so far, still looking. Anyone?
Datum: Thu, 19 Jun 2008 20:33:43 +0900
Von: Casimir <[email protected]>
An: (e-mail address removed)
Betreff: Texture/image similarity with ruby? (computer vision)
Still no luck so far, still looking. Anyone?
Casimir said:Still no luck so far, still looking. Anyone?
well, what does similarity of two images/textures mean for you ?
Given a (hopefully large) set of images, you could divide it into a
training set and a set of images to be classified into sets of mutually
similar images. One way to perform both the training and the classification
is using Support Vector Machines. I found these Ruby bindings to a library
providing these by a Google search, haven't used them yet :
http://sourceforge.net/projects/rubysvm/
Most probably, you'll need to read out image information at the pixel level at some point.
Imagemagick is a very powerful library to do this, and Tim Hunter provides
a wonderfully rich Ruby binding to it: RMagick.
Psychologists who have conducted image similarity studies compare many different
measures and filtering methods,e.g., here:
@misc{ karl-perception,
author = "Dirk Neumann Karl",
title = "Perception Based Image Retrieval",
url = "citeseer.ist.psu.edu/635475.html" },
At some point, most of these methods use a (discrete) Fourier transform of some of the
image information and compare the results of the transforms of two images to assess their
similarity.
You could use Ruby-GSL, or Narray with fftw3 to perform that.
Axel Etzold wrote on Thu, 19 Jun 2008 20:33:43 +0900
Perceptual similarity as a human subject would experience it. Let me expand
(=ramble) on this:
At the moment I am focusing on following the problem: Given any single
photograph and a random set of photos (20-100), which of the random set is
most similar, perceptually, to the target photo.
I have made some simple tests that divide image into color-channel
components and a luminosity channel, downsample the channels into a 16x16
arrays, and calculates the difference between the target photo to each of
the random ones. Difference hashing its called?
Results are rather confusing. Most of the time perceived similarity (as I
experience it) does not exist, even if statistically the images might be
similar. ....
What kind of computational algorithm would provide the perceptual similarity
score, rating or hash of some kind between two or more images that would
match the way humans perceive best?
Datum: Mon, 23 Jun 2008 20:08:13 +0900
Von: Casimir <[email protected]>
An: (e-mail address removed)
Betreff: Re: Texture/image similarity with ruby? (computer vision)
Axel Etzold wrote on Thu, 19 Jun 2008 20:33:43 +0900
Perceptual similarity as a human subject would experience it.
At the moment I am focusing on following the problem: Given any single
photograph and a random set of photos (20-100), which of the random set
is most similar, perceptually, to the target photo.
I have made some simple tests that divide image into color-channel
components and a luminosity channel, downsample the channels into a
16x16 arrays, and calculates the difference between the target photo to
each of the random ones. Difference hashing its called?
Results are rather confusing. Most of the time perceived similarity (as
I experience it) does not exist, even if statistically the images might
be similar.
... [Support Vector Machines] is one of the possible avenues. Gabor features sets used this kind
of approach I believe.
But, I don't see this as the most interesting approach. The particular
problem I wrestle with has rather small sets, and training would have to
be performed for every photo.
May be training a nn is the only way to really do this.
E. Borasky - Yes, I have the tools down, but clearly dont have a
suitable perceptual image comparison algo yet.
Thanks also to Ron Fox for pointing out its not going to be easy.
So, I guess I could use the rest of my lunch break to elaborate on the
Question.
What kind of computational algorithm would provide the perceptual
similarity score, rating or hash of some kind between two or more images
that would match the way humans perceive best?
I guess one would need two distinct classifications: similarity of
morphological appearance (features, shapes, ? in image) and similarity
of the colors (of the areas).
Still no luck so far, still looking. Anyone?
Still no luck so far, still looking. Anyone?
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