Hi Jim,
What Joe said is correct when you want to label/classify
images, since classifying images by trying to find similarity of the
test image with the training images on pixel level would not work even
if there is some ordinary geometric transform like scaling or rotation
or Intensity changes. This is where Feature Detection and Feature
Description algorithms mentioned above come into play. I have
implemented a few of them in scikit-image as part of this year's GSoC,
though some of them are not yet merged. OpenCV's feature2d module is
very comprehensive in such algorithms.
From your description, it seems you want to label each pixel
and hence extracting on a features pixel by pixel basis is a better
way to go. Depending on your data, the feature vector used to describe
every pixel might depend on values/properties of its neighbouring
patch say 5 x 5.It is not clear whether you have a labelled training
data. If you do, you will need to describe each pixel(i.e. data point)
using a feature vector that is discriminates well between different
labels and is similar for pixels belonging to the same label. You will
have to use the information about the dataset to figure out what
features might be appropriate to describe each pixel. If you don't
have a labelled dataset, this turns into clustering problem, where the
features you choose for representing a pixel will depend on the
motivation for clustering.
Another good free resource for Computer Vision is
http://szeliski.org/Book/
Hope this helps. Thanks.
Cheers,
Ankit Agrawal,
Communication and Signal Processing,
IIT Bombay.
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