Thanks!
I'll look into alternative ways of producing features.
-Lisa
On Monday, July 8, 2013 6:28:52 AM UTC-4, Stefan van der Walt wrote:Hi Lisa
Interestingly, Adam Wisniewski was working on this one-class
classification problem at the recent SciPy2013 sprint. Olivier Grisel
and Nelle Varoquaux from the sklearn team were able to give us some
helpful advice, and it might be worth getting in touch with them as
well.
On Wed, Jul 3, 2013 at 9:10 PM, Lisa Torrey <lisa....@gmail.com> wrote:
> - I have much less data. (Just 77 positives and 78 negatives, compared to
> Dalal's 1239 and 12180.)
You'll probably have to do some kind of cross-validation.
> - My images aren't all the same size, like the pedestrian images are. (I'm
> not sure if this would matter?)
Perhaps investigate multi-scale texture features, such as the wavelet
coefficients (see http://www.pybytes.com/pywavelets/ ; even simple
statistics might suffice).
> - My images are much higher resolution. (I've been downscaling them by a
> factor of 8, but the feature vectors are still enormous.)
You'd want to extract some features that help the classifier, e.g.
daisy (http://scikit-image.org/docs/dev/auto_examples/plot_daisy.html),
texture features via grey-level co-occurrence matrices, or haralick
features (we don't yet have those in skimage, although they are
available in Luis Coelho's Mahotas).
Regards
Stéfan