Hi,
I made a pull-request with the draft:
https://github.com/scikit-learn/scikit-learn/pull/6509
Extracting the feature is taking a honest amount of time (around 30 sec.)
The codebook generation through MiniBatchKMeans is more problematic. I am
still running it but it could be a couple of hours.
Let me know what do you think about it,
Cheers,
On 24 February 2016 at 00:41, Andy <t3k...@gmail.com> wrote:
> On 02/23/2016 04:32 PM, Guillaume Lemaitre wrote:
>
> Since that I was working on a cluster I did not realize but loading all
> the image in memory will be problematic with a laptop-desktop configuration.
>
> Or we can learn the PCA projection on a subset and to apply the dimension
> reduction right after the patch extraction. However, I am not sure that all
> data will fit in memory.
>
> We have out of core versions for PCA and KMeans.
>
> I think the way I'd do it is to go over all images, extract only a couple
> of patches from each image, store them.
> After we have some patches from all images, I'd learn the PCA model.
> Then we can go over the data again, transforming the patches. If they
> don't fit into memory after dimensionality reduction, we can
> use minibatch k-means to do the clustering without loading all the data.
> then we need to go over the data one more time to get the cluster centers
> and compute the BoW (which will fit in memory)
>
>
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