I am using CvSVM to classify only two types of facial expression. I used LBP(Local Binary Pattern) based histogram to extract features from the images, and trained using cvSVM::train(data_mat,labels_mat,Mat(),Mat(),params), where,
data_mat is of size 200x3452, containing normalized(0-1) feature histogram of 200 samples in row major form, with 3452 features each(depends on number of neighbourhood points)
labels_mat is corresponding label matrix containing only two value 0 and 1.
The parameters are:
CvSVMParams params;
params.svm_type =CvSVM::C_SVC;
params.kernel_type =CvSVM::LINEAR;
params.C =0.01;
params.term_crit=cvTermCriteria(CV_TERMCRIT_ITER,(int)1e7,1e-7);
The problem is that:-
while testing I get very bad result (around 10%-30% accuracy), even after applying with different kernel and train_auto() function.
CvSVM::predict(test_data_mat,true) gives 'NaN' output
I will greatly appreciate any help with this, it's got me stumped.
I suppose, that your classes linearly hard/non-separable in feature space you use.
May be it will be better to apply PCA to your dataset before classifier training step
and estimate effective dimensionality of this problem.
Also I think it will be userful test your dataset with other classifiers.
You can adapt for this purpose standard opencv example points_classifier.cpp.
It includes a lot of different classifiers with similar interface you can play with.
The SVM generalization power is low.In the first reduce your data dimension by principal component analysis then change your SVM kerenl type to RBF.
Related
I am working on a Time Series Dataset where i want to do forcasting and prediction both. So, if you have any suggestion please share. Thank You!
T-Smote
This allows one to both impute fully missing observations to allow uniform time series classification across the entire data and, in special cases, to impute individually missing features. To do so, we slightly generalize the well-known class imbalance algorithm SMOTE to allow component wise nearest neighbor interpolation that preserves correlations when there are no missing features. We visualize the method in the simplified setting of 2-dimensional uncoupled harmonic oscillators. Next, we use tSMOTE to train an Encoder/Decoder long-short term memory (LSTM) model with Logistic Regression for predicting and classifying distinct trajectories of different 2D oscillators.
I am new to machine learning and openCV. I have taken a set of 10 images for each emotion(neutral and happy) from Cohn-Kanade face database. Then I have extracted the facial features from each image and put them in my trainingData Matrix and assigned the label for the respective emotion (Example: 0 for neutral and 1 for happy).
I have used the RBF kernel with gamma = 0.1 and C = 1. Once trained, I am passing the facial features extracted from the live camera frames from a smartphone camera for prediction. The prediction always returns 1.
If I increase the number of training samples for neutral expression(example: 15 neutral expression images and 10 happy expression images), then the prediction always returns 0 and if there are equal number of images for each expression in the training samples, then SVM prediction always returns 1.
Why is the SVM behaving this way? How to check if I am using the right values for gamma and C? Also, does SVM depend on the resolution of training images and testing images?
I would request you to upload the SVM function so we can understand your code. Secondly, I have used SVM before and you need to normalize the training data and the labels. You should also make sure you are using the correct classifier as not all classifiers are supported. Follows this link for some tutorials http://docs.opencv.org/3.0-beta/modules/ml/doc/support_vector_machines.html
For answering your other questions, unfortunately you have to find the best combination for gamma and C yourself, which is kind of the drawback of SVM. https://www.quora.com/What-are-C-and-gamma-with-regards-to-a-support-vector-machine
Yes, the SVM does depend on the resolution as your features/feature vectors would change depending on the resolution and hence the inputs and the labels.
P.S. This should ideally be in comments but unfortunately i don't have enough points to do that.
I've got a set of F features e.g. Lab color space, entropy. By concatenating all features together, I obtain a feature vector of dimension d (between 12 and 50, depending on which features selected.
I usually get between 1000 and 5000 new samples, denoted x. A Gaussian Mixture Model is then trained with the vectors, but I don't know which class the features are from. What I know though, is that there are only 2 classes. Based on the GMM prediction I get a probability of that feature vector belonging to class 1 or 2.
My question now is: How do I obtain the best subset of features, for instance only entropy and normalized rgb, that will give me the best classification accuracy? I guess this is achieved, if the class separability is increased, due to the feature subset selection.
Maybe I can utilize Fisher's linear discriminant analysis? Since I already have the mean and covariance matrices obtained from the GMM. But wouldn't I have to calculate the score for each combination of features then?
Would be nice to get some help if this is a unrewarding approach and I'm on the wrong track and/or any other suggestions?
One way of finding "informative" features is to use the features that will maximise the log likelihood. You could do this with cross validation.
https://www.cs.cmu.edu/~kdeng/thesis/feature.pdf
Another idea might be to use another unsupervised algorithm that automatically selects features such as an clustering forest
http://research.microsoft.com/pubs/155552/decisionForests_MSR_TR_2011_114.pdf
In that case the clustering algorithm will automatically split the data based on information gain.
Fisher LDA will not select features but project your original data into a lower dimensional subspace. If you are looking into the subspace method
another interesting approach might be spectral clustering, which also happens
in a subspace or unsupervised neural networks such as auto encoder.
I am new to Text Mining. I am working on Spam filter. I did text cleaning, removed stop words. n-grams are my features. So I build a frequency matrix and build model using Naive Bayes. I have very limited set of training data, so I am facing the following problem.
When a sentence comes to me for classification and if none of its features match with the existing features in training then my frequency vector has only zeros.
When I send this vector for classification, I obviously get a useless result.
What can be ideal size of training data to expect better results?
Generally, the more data you have, the better. You will get diminishing returns at some point. It is often a good idea to see if your training set size is a problem by plotting the cross validation performance while varying the size of the training set. In scikit-learn has an example of this type of "learning curve."
Scikit-learn Learning Curve Example
You may consider bringing in outside sample posts to increase the size of your training set.
As you grow your training set, you may want to try reducing the bias of your classifier. This could be done by adding n-gram features, or switching to a logistic regression or SVM model.
When a sentence comes to me for classification and if none of its features match with the existing features in training then my frequency vector has only zeros.
You should normalize your input so that it forms some kind of rough distribution around 0. A common method is to do this tranformation:
input_signal = (feature - feature_mean) / feature_stddev
Then all zeroes would only happen if all features were exactly at the mean.
I am trying to classify the yard digits on the football field. I am able to detect them (different method) well. I have a minimal bounding box drawn around the tens place digits '1,2,3,4,5'. My goal is to classify them.
Ive been trying to train an SVM classifier on hog features I extract from the training set. A small subset of my training digits are here: http://ssadanand.imgur.com/all/
While training, I visualize my hog descriptors and they look correct. I use a 64X128 training window and other default parameters that OPencv's HOGDescriptor uses.
Once I train my images (50 samples per class, 5 classes), I have a 250X3780 training vector and 1X250 label vector which holds the class label values which I feed to a CvSVM object. Here is where I have a problem.
I tried using the default CvSVMParams() while using CvSVM. Terrible performance when tested on the training set itself!
I tried customizing my CvSVMPARAMS doing this:
CvSVMParams params = CvSVMParams();
params.svm_type = CvSVM::EPS_SVR;
params.kernel_type = CvSVM::POLY;
params.C = 1; params.p = 0.5; params.degree = 1;
and different variations of these parameters and my SVM classifier is terribly even when I test on the training set!
Can somebody help me out with parameterizing my SVM for this 5 class classifier?
I don't understand which kernel and what svm type I must use for this problem. Also, how in the world am I supposed to find out the values of c, p, degree for my svm?
I would assume this is an extremely easy classification problem since all my objects are nicely bounded in a box, fairly good resolution, and the classes i.e.: the digits 1,2,3,4,5 are fairly unique in appearance. I don't understand why my SVM is doing so poorly. What am I missing here?
A priori and without experimentation, it's very hard to give you some good parameters but I can give you some ideas.
First, you want to model a multi class classifier but you are using a regression algorithm, not that you can't do that but usually is easier if you start with C-SVM first.
Second, I would recommend to use RBF instead of a Polynomial kernel. Poly is very hard to get it right and usually RBF would do a better job out of the box.
Third, I would play with several values of C, don't be shy and try a bigger C (such as 100) which would force the algorithm to pick more SVs. It can lead to overfitting but if you can't even make the algorithm to learn the training set that's not your immediate problem.
Fourth, I would reduce the dimension of the images at first and then if needed, when you have a more stable model, you could try with that dimension again.
I really recommend you to read LibSVM guide which is very easy to follow http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Hope it helps!
EDIT:
I forgot to mention, that a good way to pick parameters for SVM is to perform cross-validation: http://en.wikipedia.org/wiki/Cross-validation_(statistics)
http://www.autonlab.org/tutorials/overfit10.pdf
http://www.youtube.com/watch?v=hihuMBCuSlU
http://www.youtube.com/watch?v=m5StqDv-YlM
EDIT2:
I know is silly because it's on the title of the question, but I didn't realize you were using HOG descriptors until you pointed out on the comments.