Re: [Scikit-learn-general] Implementation of Bag-of-Features

[Guillaume Lemaitre]

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. 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 From: Nadim Farhat Sent: Tuesday, 23 February 2016 20:40 To: scikit-learn-general@lists.sourceforge.net Reply To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Implementation of Bag-of-Features HI Andreas, Sorry for Jumping into the conversation and getting a bit off topic, what does it mean  "flat data" sets in sklearn ?  Bests Nadim Farhat Phd Bioengineering candidate Center for Ultrasound and therapeutics  University of Pittsburgh On Mon, Feb 22, 2016 at 12:12 PM Andreas Mueller <t3k...@gmail.com> wrote: Hi Guillaume. I was a big user of BoW myself, but I don't think it should go into scikit-learn. BoW doesn't really operate on a "flat" dataset, as scikit-learn usually does. It works on groups of data points. Each sample is usually a concatenation of feature vectors, which you summarize as a histogram. That doesn't really fit into the scikit-learn API. For any particular application (I did bag of visual words), creating an implementation using the kmeans or sparse coding in scikit-learn is only a couple of lines (you can find my visual bow for per-superpixel descriptors here https://github.com/amueller/segmentation/blob/master/bow.py#L184) Cheers, Andy On 02/14/2016 09:03 PM, Guillaume Lemaître wrote: Dear all, My group and I, are currently working on image classification applied to medical images. We are using the Bag-of-Features (or Bag-of-Visual-Words, Bag-of-Words) which was inspired originally from the text classification. In fact, we have a kind of dirty implementation [here](https://github.com/glemaitre/protoclass/blob/master/protoclass/extraction/codebook.py) which I would like to, somehow, even only if it is for a personal branch, integrate to the scikit-learn. However, I have some philosophical questions before to mess around, which in fact are feeding some discussions in our lab. Checking the API, the BoF approach could be part of the `feature_extraction` module. BoF is really similar to the implementation of the BoW for text as previously mentioned. Nevertheless, I am questioning if the BoF shall rather not be integrated to the `decomposition` module. By looking at it, the method consists of: (i) dictionary learning (base K-Means, Mean-Shift, etc.), (ii) encoding (or voting in that case using k-NN), and (iii) pooling (histogram). Thus, in some sort the BoF can be seen as any of the decomposition (even more similar to sparse coding). For instance the sparse learning follow exactly the same scheme: dictionary learning with K-SVD, encoding, and pooling (min/max/etc.). Similar thing for PCA, if you tackle the problem of dictionary as finding the eigenvectors/eigenvalues. My questions are thus the following: - what are you thinking about such thing; - where the BoF implementation of this approach is the most judicious; - would it be judicious to think about the different decomposition methods as the three steps earlier mentioned or it would be not at all intuitive? Hope that the topic is not to weird. Cheers, -- LEMAÎTRE Guillaume PhD Candidate MSc Erasmus Mundus ViBOT (Vision-roBOTic) MSc Business Innovation and Technology Management g.lemaitr...@gmail.com ViCOROB - Computer Vision and Robotic Team Universitat de Girona, Campus Montilivi, Edifici P-IV 17071 Girona Tel. +34 972 41 98 12 - Fax. +34 972 41 82 59  http://vicorob.udg.es/ LE2I - Le Creusot IUT Le Creusot, Laboratoire LE2I, 12 rue de la Fonderie, 71200 Le Creusot Tel. +33 3 85 73 10 90 - Fax. +33 3 85 73 10 97  http://le2i.cnrs.fr          https://sites.google.com/site/glemaitre58/ Vice - Chairman of A.S.C. Fours UFOLEP Chairman of A.S.C. 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