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So, I'm interested in learning how to detect patterns in a set of images and then use those patterns to create a new image of similar style. For example, say there is a group of 20~ish images (hopefully a large enough sample) with some interesting characteristics and one wished to produce a new image of a set size with such characteristics for an application's background. The catch is that the background should be different each time it is loaded, so it must be generated - not produced in an image editing software.

Now, once I've the base concepts down, I should be able to write something genetic to spit out images until I like the results, but I'm not sure how to start researching this one. I can't really use fractal algorithms because I want to avoid the repeating behavior. So, which metrics do you think I should obtain, and do you know of any good resources for using said metrics to create new images that incorporate the patterns/tendencies of an image set?

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It is not a difficult task but needs quite much effort to get such a good algorithm. There are quite many segmentation algorithm with which you can determine similar contents. What do you mean by metrics? can you give an example or elaborate on this? – Seyhmus Güngören Aug 14 '12 at 14:41
"Similar style" is a very fuzzy concept to nail down, but you might find it interesting to follow some links from here. – Henning Makholm Aug 14 '12 at 14:44
There are several image metrics (none of which I am too familiar with), such as line profiles, hue/saturation measurements and greyscale intensity. I'm sorry about the vagueness of 'style;' I'm asuming that after deciding what I need to measure to get pointed in the right direction, I'll need to plug the values into a genetic algorithm to 'nudge' the results into the exact output I'm looking for. Thanks for the links; this will make a great start! – bendicott Aug 14 '12 at 17:13

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