| Ok so we can retake part of the dictionary learning http://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html#example-cluster-plot-dict-face-patches-py I will check what it can give. Guillaume Lemaitre PhD candidate MSc Erasmus Mundus Vision and robotic (ViBOT) Master in Business Innovation and Technology Management (BITM) Univertité de Bourgogne - LE2I Universitat de Girona - ViCOROB
On 02/23/2016 04:32 PM, Guillaume
Lemaitre wrote:
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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