C
Casimir
Still no luck so far, still looking. Anyone?
A
Axel Etzold
Hi --
-------- Original-Nachricht --------
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.
Best regards,
Axel
-------- Original-Nachricht --------
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.
Best regards,
Axel
M
M. Edward (Ed) Borasky
Casimir said:
First step -- define the image processing algorithms that do what you want.
Second step - find a library or package that does them
Third step - if it's ImageMagick, use RMagick. If not, you may have to
write a wrapper to use Ruby.
R
Ron Fox
Uh well this is a >HARD< research project. I would not expect an
answer here... unless there are some people in computer vision
floating here. If you have a local college with a _good_ computer
science dept. that has someone that does computer vision.. make an
appointment with them.
Be prepared to describe exactly what you want to achieve. Be
prepared for it not to be easy.
Ron.
answer here... unless there are some people in computer vision
floating here. If you have a local college with a _good_ computer
science dept. that has someone that does computer vision.. make an
appointment with them.
Be prepared to describe exactly what you want to achieve. Be
prepared for it not to be easy.
Ron.
C
Casimir
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.
IOW, random bw-noise-image gets rather good similarity scores whilst
morphologically having zero resemblance to the target photo.
But anyone who has studied this subject even superficially already knows
this - hashing wont produce the same kind of similarity we experience
with our senses.
This is one of the possible avenues. Gabor features sets used this kind
of approach I believe. Thank you for that link.
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. Not really practical or efficient. May be
training a nn is the only way to really do this.
I have been using RMagick, AND have been looking at Hornets Eye, which
might be useful at some point, but at the moment doesn't cater to this
specific case.
Too bad that service isnt available, as I have not read that article.
Narray is what I was planning to use once theres a working model for the
similarity comparison in place.
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).
I assume there is no answer in existance to this question, well, that I
know of.
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.
IOW, random bw-noise-image gets rather good similarity scores whilst
morphologically having zero resemblance to the target photo.
But anyone who has studied this subject even superficially already knows
this - hashing wont produce the same kind of similarity we experience
with our senses.
This is one of the possible avenues. Gabor features sets used this kind
of approach I believe. Thank you for that link.
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. Not really practical or efficient. May be
training a nn is the only way to really do this.
I have been using RMagick, AND have been looking at Hornets Eye, which
might be useful at some point, but at the moment doesn't cater to this
specific case.
Too bad that service isnt available, as I have not read that article.
Narray is what I was planning to use once theres a working model for the
similarity comparison in place.
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).
I assume there is no answer in existance to this question, well, that I
know of.
K
Kyle Schmitt
If I'm recalling properly, a lot of research has had success doing
histogram comparison. More or less it's showing images being similar
based on similar color composition. That sounds silly, but in reality
it's quite effective for a first go at it.
You mention a training library, and since a good deal of computer
vision is still crossing over from AI, an important question becomes,
what type of system will be doing the learning?
--Kyle
A
Axel Etzold
-------- Original-Nachricht --------
Dear Casimir,
As a scientific folklore has it , if you haven't got the answer, it's because you did not understand
the question ...
I think this statement is not (yet) useful, since there is no such thing as a unique perception of human
subjects of an image. A subset of images in your collection might be similar because they all show postcard destinations (the skyline of Manhattan, Big Ben in London, a camel in front of the Pyramids in Cairo, a koala in Australia, a Tyrolean yodler), but the same images show living beings whereas others show architecture and therefore are dissimilar to a psychologist or to a civil engineer ...
I know of many neuroscientists who train monkeys, usually for at least six months, more often for a year, on some very specific task to test a particular hypothesis about early vision. (It takes this long, because you can't explain the monkey
what to do in English, they'll tell you). These monkeys then become experts on any weird task -- theoretical computer science holds some nice, albeit non-constructive (in the sense of their mathematical proof style) theorems about computability using neural networks, that can be confirmed experimentally this way.
So there is a description problem that will possibly lead you down very different ways on the same particular set of data, depending on whether you are a psychologist or a civil engineer or a tourist manager in the example above.
On the other hand, there is the anecdote of a journalist questioning a judge at Supreme Court of the United States to define obscenity (right after a decision the journalist didn't like).
Journalist: How do you define obscenity?
Judge: I recognise it when I see it.
So given the preliminary information at breakfast that we need to book the summer holidays, we might all classify the
data from the example above the way the tourist manager would as we sit in the travel agent's shop, there is still a question of how to do that.
This shows that apparently, the human visual system does not use this kind
of downsampling for similarity measures....
Gabor feature sets (or wavelets in general) and support vector machines and I'd claim, any
feasible and plausible approach of modelling vision will need some kind of data reduction ...
reconstruct an image with loss using basis functions, use only a few, but yet so many that
the loss is not (annoyingly) perceptible. The number of parameters you'll need to do that
will then suffice to do the classification also.
Interpersonal mileage may vary.. there are some individuals who cannot distinguish Bordeaux
from Bourgogne when tasting either, but then they don't write wine connoissor guides.
That's in contradiction to your statement above ... if there is such a thing as human similarity
perception, it should hold for all cases brought in front of you.
In other words, if you are a judge at a supreme court, you should not confuse and irritate
people by taking decisions that seem totally contradictory all the time ... those people that do not like the decisions
taken will still appreciate internal consistency ...
Neural networks can be seen as a hardware for support vector machines or for Gabor patches etc (even if
this is a gedankenexperiment most of the time).
I would take an iterative approach : classify the pictures that look similar to you by hand and then plot the classes as point clusters in different colours in the space of variables which you think seem useful (colour, hue, ...)
Can you easily draw a line to separate the dots of different classes ? Then the SVM in that particular
space delivers what you want.
Otherwise, introducing a new dimension might give you the opportunity to introduce a separating plane,
or you might still need another one. Eventually, there's the theorem of Hahn-Banach from functional
analysis that shows how to separate points ... any set of points ... using a hyperplane (i.e., something linear
and therefore computationally well-behaved).
A good idea would be to have a look at a good introduction to wavelets ...
http://www.gvsu.edu/math/wavelets/tutorials.htm
You can use wavelets as basis functions, fit parameters for your data and then try to find
separable parameter sets for your different classes.
Think of something plausible when choosing ... as Ron Fox said, it's not easy ...
Best regards,
Axel
Dear Casimir,
As a scientific folklore has it , if you haven't got the answer, it's because you did not understand
the question ...
I think this statement is not (yet) useful, since there is no such thing as a unique perception of human
subjects of an image. A subset of images in your collection might be similar because they all show postcard destinations (the skyline of Manhattan, Big Ben in London, a camel in front of the Pyramids in Cairo, a koala in Australia, a Tyrolean yodler), but the same images show living beings whereas others show architecture and therefore are dissimilar to a psychologist or to a civil engineer ...
I know of many neuroscientists who train monkeys, usually for at least six months, more often for a year, on some very specific task to test a particular hypothesis about early vision. (It takes this long, because you can't explain the monkey
what to do in English, they'll tell you). These monkeys then become experts on any weird task -- theoretical computer science holds some nice, albeit non-constructive (in the sense of their mathematical proof style) theorems about computability using neural networks, that can be confirmed experimentally this way.
So there is a description problem that will possibly lead you down very different ways on the same particular set of data, depending on whether you are a psychologist or a civil engineer or a tourist manager in the example above.
On the other hand, there is the anecdote of a journalist questioning a judge at Supreme Court of the United States to define obscenity (right after a decision the journalist didn't like).
Journalist: How do you define obscenity?
Judge: I recognise it when I see it.
So given the preliminary information at breakfast that we need to book the summer holidays, we might all classify the
data from the example above the way the tourist manager would as we sit in the travel agent's shop, there is still a question of how to do that.
This shows that apparently, the human visual system does not use this kind
of downsampling for similarity measures....
Gabor feature sets (or wavelets in general) and support vector machines and I'd claim, any
feasible and plausible approach of modelling vision will need some kind of data reduction ...
reconstruct an image with loss using basis functions, use only a few, but yet so many that
the loss is not (annoyingly) perceptible. The number of parameters you'll need to do that
will then suffice to do the classification also.
Interpersonal mileage may vary.. there are some individuals who cannot distinguish Bordeaux
from Bourgogne when tasting either, but then they don't write wine connoissor guides.
That's in contradiction to your statement above ... if there is such a thing as human similarity
perception, it should hold for all cases brought in front of you.
In other words, if you are a judge at a supreme court, you should not confuse and irritate
people by taking decisions that seem totally contradictory all the time ... those people that do not like the decisions
taken will still appreciate internal consistency ...
Neural networks can be seen as a hardware for support vector machines or for Gabor patches etc (even if
this is a gedankenexperiment most of the time).
I would take an iterative approach : classify the pictures that look similar to you by hand and then plot the classes as point clusters in different colours in the space of variables which you think seem useful (colour, hue, ...)
Can you easily draw a line to separate the dots of different classes ? Then the SVM in that particular
space delivers what you want.
Otherwise, introducing a new dimension might give you the opportunity to introduce a separating plane,
or you might still need another one. Eventually, there's the theorem of Hahn-Banach from functional
analysis that shows how to separate points ... any set of points ... using a hyperplane (i.e., something linear
and therefore computationally well-behaved).
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).
A good idea would be to have a look at a good introduction to wavelets ...
http://www.gvsu.edu/math/wavelets/tutorials.htm
You can use wavelets as basis functions, fit parameters for your data and then try to find
separable parameter sets for your different classes.
Think of something plausible when choosing ... as Ron Fox said, it's not easy ...
Best regards,
Axel
C
Chris Hulan
Have you seen Camellia (http://camellia.sourceforge.net/index.html)?
Its implemented in C but has been wrapped for Ruby
May proivide some of the functionality you need.
Also links to OpenCVL (http://opencvlibrary.sourceforge.net)
Cheers
Chris
A
ara.t.howard
a good toolkit:
http://kogs-www.informatik.uni-hamburg.de/~koethe/vigra/
and another
http://www.itk.org/HTML/WatershedSegmentationExample.html
i've used bot a lot, shelling out to ruby mostly, unfortunately
require hacking in c--. insight has ruby bindings though (via swig).
i think you are going to want to do some filtering in the frequency
domain focusing on edge images (shapes) and colours, possibly warping
to achieve a best fit. whatever you do is going to be entirely custom
- as everything in computer visions tends to be, despite having a
plethora of off the shelf tools to start from.
kind regards.
ps.
i nearly always end up stuff pixels into narrays and doing some manip
there - if nothing else to refine algorithms in ruby before
laboriously re-coding in c-- or c. for this is useful to have
representatvie images that are small and as close to raw as possible.
a @ http://codeforpeople.com/