seeking advice on HoG applicability

[Lisa Torrey]

Ok, will do smoothing. Thanks!

On Wednesday, July 10, 2013 4:12:56 AM UTC-4, Johannes Schönberger wrote:
>
> The smoothing is applied before sub-sampling to suppress high frequencies 
> which result in aliasing effects when sub-sampling. I recommend to use the 
> Gaussian pyramid. The Laplacian pyramid shows you the difference between 
> the smoothed and original image (the suppressed high frequencies) for each 
> pyramid layer, respectively. 
>
> Johannes Schönberger 
>
> Am 09.07.2013 um 21:46 schrieb Lisa Torrey <lisa.... at gmail.com<javascript:>>: 
>
>
> > A minor question that comes up as I make the images a uniform size: is 
> it better to use pyramid_reduce() rather than resize(), or does it not 
> matter? I see that pyramid_reduce() does smoothing, then calls resize(). 
> > 
> > 
> > On Monday, July 8, 2013 12:06:36 PM UTC-4, Lisa Torrey wrote: 
> > 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.... at 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 
> > 
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