I want to optimize MLP in Weka with an algorithm (for example genetic).
Would you please tell me how can I tell Weka to train my MLP with genetic algorithm?
Is it possible? If so, please tell me how.
Best Regards
Related
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
I have seen these two lines of code in may online forums but I don't understand where the SVM vector comes from, i.e. what was the training data that was used to train this SVM and can I find that data and source code anywhere?
And also why does the SVM vector have a length of 3781 for a 64x128 image?
Some insight into this would be really helpful.
Thanks
Here you are using pre-trained people detector as SVM. You can read about it in the doc. I don't know the way that they trained it (The algorithms, parameters). But according to this answer, it was trained with Daimler Pedestrian Detection Dataset.
cv2.HOGDescriptor_getDefaultPeopleDetector() will return a array with size 3781 in size. Those are coefficients that are used by SVM to classify people. It has nothing to do with the input image that you are using.
And most importantly you can train a SVM as you like to detect another object and use as the SVM detector. Check this answer for more.
I am trying openCV EigenFaceRecognizer example given here.
I have successfully recognized the face and predictedLabel is correct. But I wish to know how much confidant the program is about the prediction. I have tried this but values seems like garbage(like 1547139552). Can anyone please help in calculating the confidence level.
Is there a function in OpenCV (C++ API) to perform Wiener filtering? In this case, which is the header file?
I am looking for a function like matlab's Wiener filter. If there is none, has anyone tried to implement it with OpenCV? My goal is to reduce the noise in disparity maps.
I found C++ source code of Weiner filter is there:
http://gigadom.wordpress.com/2012/05/11/deblurring-with-opencv-weiner-filter-reloaded/
and there
https://github.com/savsun/Filters
You can simply edit it and then call as function.
Bad news: there is none.
Good news: it's not difficult to implement one with the classical equations, using OpenCV's FFT functions. It does even provide an API to multiply spectrums.
Now, you may also try other algorithms such as median filtering or implement TV denoising that have been shown to work with depth maps.
I know that this is an old question but I've encountered the same need few days ago. I wrote my personal C++ implementation of the adaptive Wiener filter (similar to the wiener2 Matlab's function) based on OpenCV library and I've pushed it on github. Hope this helps!
You can try to implement by yourself the Wiener filter, for example the book
PETROU, Maria; PETROU, Costas. Image processing: the fundamentals. John Wiley & Sons, 2010.
has a full derivation of the formula for the Wiener filter and a lot of suggestions and practical explanation for implementing the algorithm (for example it explains how to estimate the power spectrum of the noise and the power spectrum of the original undegraded image/signal starting just from the degraded and noisy image/signal and explaining well some reasonable assumptions).
I found a tutorial about VLFeat HOG
http://www.vlfeat.org/overview/hog.html
I am a little confused by the 16*16*31 matrix. Can anyone tell me how can I extract features that can be used for classification task from the matrix that the function returns?
Thanks!
The entries in that matrix are features! Depending on what you're trying to achieve you might do some dimensionality reduction or augmentation or post processing, but none of that is strictly necessary. Check out the original HoG paper.
I am currently working on SIFT using Matlab. I had implemented the Scale-Space and DoG. Currently working on Keypoint localization. I am able to extract the maxima and minima. How should I proceed from here? Besides that, anyone is interest to verify my coding?
http://www.ipol.im/pub/pre/82/preprint.pdf . This paper gives a detailed analysis of SIFT . This should clarify most of your implementation based queries