Stojan Dimitrovski
2013-04-23 12:39:57 UTC
Hello everyone!
I'm a sophomore from Macedonia and am thinking about applying for the
Media Fingerprinting Library idea you have put on your Wiki.
I don't have much experience with algorithms like this, but I think I
have much of the fundamentals down. I'm fluent with C / C++,
JavaScript and node.js. In fact, here is my Github:
http://github.com/hf which you can check out.
I have been thinking on how to derive fingerprints from pixel images
and I may have come up with a way.
Simply put, the idea is this: The distinguishing elements in a pixel
image are regions of color. When two different regions of color
appear, an edge is formed. Those edges are what make up shapes and
other perceptual elements we as humans see. Now, a statistical model
can be built from that image that almost uniquely identifies the
image.
Roughly, the statistical model is derived like this: Firstly the pixel
data is separated into Hues, Values and Saturation. Then, from those
three separate sets of values, we compute a histogram. The histogram
now uniquely represents those surfaces of HSV values in the image.
Now, we need to figure a way out to quantify the shapes in the image.
What I'm thinking is that in those three sets of pixel data a
convolution operation is done in such a way that generates some form
of edge-detection. Now, the histograms are updated using a weighting
factor that increases the occurrence of a pixel that is nearer to an
egde. That way, the HSV values around the edges will have higher
values in the histogram, rather than those that are not.
The resulting histograms can be converted into a hash. (This part I
have not yet figured out.)
To compare two images, is to run the histograms on both and compare
their hashes.
Why this method may work: by dealing with raw pixel data and
histograms we allow for transformations on the pixel data. Skews,
rotations, blurs (to an extent), will generally produce the same
histogram. And since we will be deriving the histograms from the Hues,
Saturation and Value channels of the pixel data, I think that changes
in color, contrast, etc. will not have a great impact.
Again, this is just a rough idea.
Tell me what you think about this?
Sincerely,
Stojan
I'm a sophomore from Macedonia and am thinking about applying for the
Media Fingerprinting Library idea you have put on your Wiki.
I don't have much experience with algorithms like this, but I think I
have much of the fundamentals down. I'm fluent with C / C++,
JavaScript and node.js. In fact, here is my Github:
http://github.com/hf which you can check out.
I have been thinking on how to derive fingerprints from pixel images
and I may have come up with a way.
Simply put, the idea is this: The distinguishing elements in a pixel
image are regions of color. When two different regions of color
appear, an edge is formed. Those edges are what make up shapes and
other perceptual elements we as humans see. Now, a statistical model
can be built from that image that almost uniquely identifies the
image.
Roughly, the statistical model is derived like this: Firstly the pixel
data is separated into Hues, Values and Saturation. Then, from those
three separate sets of values, we compute a histogram. The histogram
now uniquely represents those surfaces of HSV values in the image.
Now, we need to figure a way out to quantify the shapes in the image.
What I'm thinking is that in those three sets of pixel data a
convolution operation is done in such a way that generates some form
of edge-detection. Now, the histograms are updated using a weighting
factor that increases the occurrence of a pixel that is nearer to an
egde. That way, the HSV values around the edges will have higher
values in the histogram, rather than those that are not.
The resulting histograms can be converted into a hash. (This part I
have not yet figured out.)
To compare two images, is to run the histograms on both and compare
their hashes.
Why this method may work: by dealing with raw pixel data and
histograms we allow for transformations on the pixel data. Skews,
rotations, blurs (to an extent), will generally produce the same
histogram. And since we will be deriving the histograms from the Hues,
Saturation and Value channels of the pixel data, I think that changes
in color, contrast, etc. will not have a great impact.
Again, this is just a rough idea.
Tell me what you think about this?
Sincerely,
Stojan