On 10/22/2013 03:54 PM, Ankit Agrawal wrote: > 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 > ishttp://szeliski.org/Book/ > > Hope this helps. Thanks. > Whoops I only say your mail after writing mine ;)
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