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Old 07-26-2012, 09:45 PM
Ubermensch Ubermensch is offline
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Default Machine learning with vector images

In the digit recognition example, the pixels of the image are split into two features, density and symmetry, to identify the digits. In case of raster graphics, the pixels are the primary unit of which ML could be done and they are uniform.

But in case of vector graphics like SVG, or co-ordinate systems, the co-ordinates and paths are explicit or denoted by a mathematical function. How could the image recognition analogy apply to this problem?

Taking the example of a SVG file, I can parse the co-ordinates and path transformations but how could I put them into matrix form since each SVG file would have different number of co-ordinates and path transformations (and of course attributes for color,strokes, etc).
Its better to look dumb and be clear rather than look smart and be unclear.
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machine learning, vector images

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