Object detection in images (HOG)

Snowflake luecks at gmail.com
Thu Apr 23 01:49:24 EDT 2015


Hello Juan!

Thank you for your reply! I am sorry about the technical problem, Google 
told me that I am signed up for this group, I did not realize. I hope this 
message will be recognized as a member. 

I really appreciate your tips and experience. However, I have one concern 
about using only intensity/color. I have several images, were the cell and 
the object are very light stained and others with objects which I don't 
want to detect are very dark stained, that's why I used HOG (the object 
which I am looking for has always kind of finger structure). I am giving it 
a try at the moment with Lab features and I will see :-)

Thanks a lot for the cross validation tip and how many images to use, this 
was very helpful.

Cheers,
Stefanie


Am Mittwoch, 22. April 2015 12:42:25 UTC+2 schrieb Juan Nunez-Iglesias:
>
> Hello!
>
> Firstly, please sign up to the mailing list before posting — if you don't, 
> every post from you has to be manually filtered through.
>
> On to your problem!
>
> So, it looks like there should be plenty of signal to distinguish between 
> object/no-object. It's key to understand the features you're using. HOG may 
> not be appropriate here: it measures gradients, not image intensity/color. 
> In this case, it looks like there will be many more dark pixels in the 
> object images. What I would do based on the examples you showed is to just 
> take Lab-transformed image and then do a histogram, and use the histogram 
> as the feature vector.
>
> You have a lot of labelled images, so use them! I would split your set 
> into 40k training / 10k test, then do 4-fold cross-validation on the 
> training set. scikit-learn has nice classes for doing cross-validation 
> automatically.
>
> As to the choice of classifier, it might be worth asking their list, but 
> *by far* the easiest to use "out-of-the-box", without fiddling with 
> parameters, is the Random Forest.
>
> Hope that helped!
>
> Juan.
>
>
>
>
> On Wed, Apr 22, 2015 at 8:21 PM, Snowflake <lue... at gmail.com <javascript:>
> > wrote:
>
>>  Hi!
>>
>> I am new to machine learning and I need some help.
>>
>>  I want to detect objects inside cells of microscopy images. I have a 
>> lot of annotated images (app. 50.000 images with an object and 500.000 
>> without an object).
>>
>> So far I tried to extract features using HOG and classifying using 
>> logistic regression and LinearSVC. I have tried several parameters for HOG 
>> or color spaces (RGB, HSV, LAB) but I don't see a big difference, the 
>> predication rate is about 70 %.
>>
>> I have several questions. How many images should I use to train the 
>> descriptor? How many images should I use to test the prediction?
>>
>> I have tried with about 1000 images for training, which gives me 55 % 
>> positive and 5000, which gives me about 72 % positive. However, it also 
>> depends a lot on the test set, sometimes a test set can reach 80-90 % 
>> positive detected images.
>>
>> Here are two examples containing an object and two images without an 
>> object:
>>
>> Object01 <http://labtools.ipk-gatersleben.de/ML/with_object01.jpg>
>>  object02 <http://labtools.ipk-gatersleben.de/ML/with_object03.jpg>
>>
>> cell01 <http://labtools.ipk-gatersleben.de/ML/cell01.jpg>
>>
>> cell02 <http://labtools.ipk-gatersleben.de/ML/cell02.jpg>
>>
>> Another problem is, sometimes the images contain several objects:
>>
>> objects <http://labtools.ipk-gatersleben.de/ML/with_object02.jpg>
>>
>>  Should I try to increase the examples of the learning set? How should I 
>> choose the images for the training set, just random? What else could I try?
>>
>> Any help or tips would be very appreciated, thank you very much in 
>> advance!
>>
>>
>>
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