[scikit-learn] Regarding Adaboost classifier

[Afzal Ansari]

Thank you for your quick kind response. You got what I exactly want to
know. Now I can expect my pre-processing methods are to be done. And also I
have got clear now from this sklearn can help you with the
AdaBoostClassifier, ranking of the features, and the evaluation of the
pipeline.


On Sun, Mar 19, 2017 at 3:46 PM, Guillaume Lemaître <g.lemaitre58 at gmail.com>
wrote:

> I want just to recap a few things:
>
> > I need to train the classifier using adaboost classifier to obtain Haar
> features from image dataset
> > So can you suggest any method to extract features from image(24*24)
> datase
>
> You just mentioned what was your requirement regarding the feature to
> extract -> Haar features.
> My feeling is that you want to reimplement the paper of Viola and Jones
> for face detection.
>
> So you could check with the folks of scikit-image if they have something
> related -> https://github.com/scikit-image/scikit-image/pull/1444
> You could also check opencv which offer functions, classe, and helper ->
> http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html /
> http://docs.opencv.org/2.4/modules/objdetect/doc/cascade_
> classification.html
>
> At the end, sklearn can help you with the AdaBoostClassifier, ranking of
> the features, and the evaluation of the pipeline.
>
>
> On 19 March 2017 at 07:57, Afzal Ansari <b113053 at iiit-bh.ac.in> wrote:
>
>> Thank you for your response. First I want to extract useful features from
>> images so as to get n_features. So can you suggest any method to extract
>> features from image(24*24) dataset? Then I can possibly train the
>> classifier.
>>
>> Thanks.
>>
>> On Sun, Mar 19, 2017 at 11:49 AM, Jacob Schreiber <
>> jmschreiber91 at gmail.com> wrote:
>>
>>> You really need to provide more details with what exactly you're stuck
>>> with. If you've extracted useful features from some image into a matrix X
>>> with binary labels y you can just do `clf.fit(X, y)` to train the
>>> classifier.
>>>
>>> On Sat, Mar 18, 2017 at 10:21 PM, Afzal Ansari <b113053 at iiit-bh.ac.in>
>>> wrote:
>>>
>>>> Hello Sir,
>>>>  I want to classify images containing negative and positive samples
>>>> using Adaboost classifier. So how can I do that classification? Please help
>>>> me regarding this.
>>>>
>>>> Thanks.
>>>>
>>>> On Sat, Mar 18, 2017 at 11:03 PM, Francois Dion <
>>>> francois.dion at gmail.com> wrote:
>>>>
>>>>> You need to provide more details on exactly what you need. I'll take a
>>>>> stab at it:
>>>>>
>>>>> Are you trying to replicate OpenCV cascade training?
>>>>> If so, what they call DAB is Scikit learn adaboostclassifier (
>>>>> http://scikit-learn.org/stable/modules/generated/sklearn.en
>>>>> semble.AdaBoostClassifier.html)‎ with algorithm=SAMME.
>>>>> RAB is SAMME.R.
>>>>>
>>>>>
>>>>> ‎Francois
>>>>>
>>>>>
>>>>> Sent from my BlackBerry 10 Darkphone
>>>>> *From: *Afzal Ansari
>>>>> *Sent: *Saturday, March 18, 2017 00:51
>>>>> *To: *scikit-learn at python.org
>>>>> *Reply To: *Scikit-learn user and developer mailing list
>>>>> *Subject: *[scikit-learn] Regarding Adaboost classifier
>>>>>
>>>>> Hello Developers!
>>>>>  I am currently working on feature extraction method which is based on
>>>>> Haar features for image classification. I am unable to find pure
>>>>> implementation of adaboost classifier algorithm on the internet even on
>>>>> scikit learn web. I need to train the classifier using adaboost classifier
>>>>> to obtain Haar features from image dataset.
>>>>> Please help me regarding this code. Reply soon.
>>>>>
>>>>> Thanks in advance.
>>>>>
>>>>>
>>>>> _______________________________________________
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>>>>> scikit-learn at python.org
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>>>>>
>>>>>
>>>>
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>>
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>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
>
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