Hi Dennis, A hopefully-helpful tidbit:
Multiple cells per column offer variable order memory, while one cell per column offers only first-order memory. However, first-order memory is useful for static invariant pattern recognition, since it doesn't maintain long context. This might be ideally suited for the image categorization problem. >From the whitepaper: "We have further observed that a region like this exhibits stability to translation, changes in scale, etc. while maintaining the ability to distinguish between different images. This behavior is what is needed for spatial invariance (recognizing the same pattern in different locations of an image)." So maybe try a one-cell-per-column region! - Chetan On Thu, Nov 14, 2013 at 11:23 AM, Dennis Stark <[email protected]> wrote: > Hello everyone, > > This is my first time writing to the mailing list, so nice to meet you > > I'm trying to solve a problem of content analysis the following way: > > 1) Break image into object > 2) Ask user to categorize objects > 3) Learn categories for those objects and use this knowledge for future > inference of new visual input. > > The biggest problem I'm facing at the moment is to make sure that object > is remembered in invariant state. Once I got my image through OpenCV, I can > fix the size of the object, but not the rotation, so in my case I need HTM > to remember rotation invariant representation of the object. HTM should > also know that this is the same object. > > So my problem at the moment is that I don't think I quite understand what > the structure of HTM should be to allow for this. Scale and rotation would > be even better. If someone has any thoughts on that - I'd really appreciate > it. > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >
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