Shall I download the dataset through the module dataset or just fetch
directly in the example? If so, do you have an example that I can see the
desired structure.
Cheers,
On 29 February 2016 at 21:56, Andreas Mueller <t3k...@gmail.com> wrote:
> Can you open a PR with an example and attach the plots and output and
> timing?
> The example needs to download the dataset.
>
> Cheers,
> Andy
>
>
> On 02/29/2016 03:15 PM, Guillaume Lemaître wrote:
>
> Hi guys,
>
> I got a bit of time meanwhile in a conference:
> <https://github.com/glemaitre/bow-sklearn-example>
> https://github.com/glemaitre/bow-sklearn-example
> The extraction is in the source. I have to include the part to measure the
> timing. Let me know of that make sense.
>
> Cheers,
>
> On 23 February 2016 at 20:39, Nadim Farhat <nadim.far...@gmail.com> wrote:
>
>> 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>
>>> 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>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 <%2B34%20972%2041%2098%2012> - Fax. +34 972 41 82
>>> 59
>>> <http://vicorob.udg.es/>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 <%2B33%203%2085%2073%2010%2090> - Fax. +33 3 85
>>> 73 10 97 <%2B33%203%2085%2073%2010%2097>
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>>>
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>
>
> --
>
>
>
>
> *LEMAÎTRE Guillaume PhD Candidate MSc Erasmus Mundus ViBOT
> (Vision-roBOTic) MSc Business Innovation and Technology Management *
> <g.lemaitr...@gmail.com>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. Fours FFC
> Webmaster of http://ascfours.free.fr
>
>
> ------------------------------------------------------------------------------
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--
*LEMAÎTRE GuillaumePhD CandidateMSc 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. Fours FFC
Webmaster of http://ascfours.free.fr
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