Guillaume Lemaître
2016-02-15 02:03:09 UTC
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,
--
*LEMAÃTRE GuillaumePhD CandidateMSc Erasmus Mundus ViBOT
(Vision-roBOTic)MSc Business Innovation and Technology Management*
***@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
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,
--
*LEMAÃTRE GuillaumePhD CandidateMSc Erasmus Mundus ViBOT
(Vision-roBOTic)MSc Business Innovation and Technology Management*
***@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