Developer discussion for Creative Commons technology and tools

[Dan Mills <dan AT creativecommons.org>]

Hi Stojan!

Sorry I missed this last week - was extremely busy.

There is a fairly substantial body of work these days on this problem space (in general), so I advise against thinking you will come up with a clever trick you can just hack up :) Check out the links in the ideas page, there are some starting points there to learn.

Dan

On Monday, April 29, 2013 at 3:13 AM, Stojan Dimitrovski wrote:

(I am resending this message since I don't think anyone got it. If you

did and ignored it, well that's OK too.)

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

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