I am currently working on a project where I have to extract the facial expression of a user (only one user at a time from a webcam) like sad or happy.
My method for classifying facial expressions is:
Use opencv to detect the face in the image
Use ASM and stasm to get the facial feature point
and now i'm trying to do facial expression classification
is SVM a good option ? and if it is how can i start with SVM :
how i'm going to train svm for every emotions using this landmarks ?
Yes, SVMs have been numerously shown to perform well in this task. There have been dozens (if not hundreads) of papers describing such procedures.
For example:
Simple paper
Longer paper
Poster about it
More complex example
Some basic sources of the SVMs themselves can be obtained on http://www.support-vector-machines.org/ (like books titles, software links etc.)
And if you are just interested in using them rather then understanding you can get one of basic libraries:
libsvm http://www.csie.ntu.edu.tw/~cjlin/libsvm/
svmlight http://svmlight.joachims.org/
if you are already using opencv,i suggest you use the built in svm implementation, training/saving/loading in python is as follow. c++ has corresponding api to do the same in about the same amount of code. it also has 'train_auto' to find best parameters
import numpy as np
import cv2
samples = np.array(np.random.random((4,5)), dtype = np.float32)
labels = np.array(np.random.randint(0,2,4), dtype = np.float32)
svm = cv2.SVM()
svmparams = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
C = 1 )
svm.train(samples, labels, params = svmparams)
testresult = np.float32( [svm.predict(s) for s in samples])
print samples
print labels
print testresult
svm.save('model.xml')
loaded=svm.load('model.xml')
and output
#print samples
[[ 0.24686454 0.07454421 0.90043277 0.37529686 0.34437731]
[ 0.41088378 0.79261768 0.46119651 0.50203663 0.64999193]
[ 0.11879266 0.6869216 0.4808321 0.6477254 0.16334397]
[ 0.02145131 0.51843268 0.74307418 0.90667248 0.07163303]]
#print labels
[ 0. 1. 1. 0.]
#print testresult
[ 0. 1. 1. 0.]
so you provide the n flattened shape models as samples and n labels and you are good to go. you probably dont even need the asm part, just apply some filters which are sensitive to orientation like sobel or gabor and concatenate the matrices and flatten them then feed them directly to svm. you probably can get maybe 70-90% accuracy.
as someone said cnn are an alternative to svms.here's some links that implement lenet5. so far,i find svms much simpler to get started.
https://github.com/lisa-lab/DeepLearningTutorials/
http://www.codeproject.com/Articles/16650/Neural-Network-for-Recognition-of-Handwritten-Digi
-edit-
landmarks are just n (x,y) vectors right? so why dont you try put them into a array of size 2n and simply feed them directly to the code above?
for example,3 training samples of 4 land marks (0,0),(10,10),(50,50),(70,70)
samples = [[0,0,10,10,50,50,70,70],
[0,0,10,10,50,50,70,70],
[0,0,10,10,50,50,70,70]]
labels=[0.,1.,2.]
0=happy
1=angry
2=disgust
You could check this code to get idea how this could be done using SVM.
You can find algorithm explained here
Related
I want to check if a clustering would be helpful or not on my coordinates.
I'm dealing with trajectories and want to check if all of them are starting on a same area (the trajectories are different). Thus the aim here is to characterise the most frequent departure points.
However, sometimes there is no need for clustering. I'm using K-means here. I had thought of using the Silhouette Score but I don't see if it is mathematically correct for the case where there is only one cluster. DBScan will not be a good clustering as density are not similar in the clusters I wanted to build.
Would you have an idea to create a kind of check between k=1 and k=3, which would be the best split for my data? I'm dealing here with data with coordinates (latitude/longitude) where the starting point is not 100% fixed but can vary within 2km around a kind of barycentre.
Simple extract with k=2 :
from pyspark.ml.feature import VectorAssembler
vecAssembler = VectorAssembler(inputCols=["lat", "lon"], outputCol="features")
df1= vecAssembler.transform(df)
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
# Loads data.
# Trains a k-means model.
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(df1.select('features'))
# Make predictions
transformed = model.transform(df1)
evaluator = ClusteringEvaluator(predictionCol='prediction', featuresCol='features', \
metricName='silhouette', distanceMeasure='squaredEuclidean')
evaluator.evaluate(transformed)
Is there a way to compute in pySpark a case with k=1 ? in order to derive Elbow or gap statistics ?
I have an image with 8 channels.I have a conventional algorithm where weights are added to each of these channels to get an output as '0' or '1'.This works fine with several samples and complex scenarios. I would like implement the same in Machine Learning using CNN method.
I am new to ML and started looking out the tutorials which seem to be exclusively dealing with image processing problems- Hand writing recognition,Feature extraction etc.
http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/
https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/neural_networks.html
I have setup the Keras with Theano as background.Basic Keras samples are working without problem.
What steps do I require to follow in order achieve the same result using CNN ? I do not comprehend the use of filters,kernels,stride in my use case.How do we provide Training data to Keras if the pixel channel values and output are in the below form?
Pixel#1 f(C1,C2...C8)=1
Pixel#2 f(C1,C2...C8)=1
Pixel#3 f(C1,C2...C8)=0 .
.
Pixel#N f(C1,C2...C8)=1
I think you should treat this the same way you use CNN to do semantic segmentation. For an example look at
https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
You can use the same architecture has they are using but for the first layer instead of using filters for 3 channels use filters for 8 channels.
For the loss function you can use the same loos function or something that is more specific for binary loss.
There are several implementation for keras but with tensorflow
backend
https://github.com/JihongJu/keras-fcn
https://github.com/aurora95/Keras-FCN
Since the input is in the form of channel values,that too in sequence.I would suggest you to use Convolution1D. Here,you are taking each pixel's channel values as the input and you need to predict for each pixel.Try this
eg :
Conv1D(filters, kernel_size, strides=1, padding='valid')
Conv1D()
MaxPooling1D(pool_size)
......
(Add many layers as you want)
......
Dense(1)
use binary_crossentropy as the loss function.
I'm very new to scikit learn and machine learning in general.
I am currently designing a SVM to predict if a specific amino acid sequence will be cut by a protease. So far the the SVM method seems to be working quite well:
I'd like to visualize the distance between the two categories (cut and uncut), so I'm trying to use the linear discrimination analysis, which is similar to the principal component analysis, using the following code:
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis(n_components=2)
targs = np.array([1 if _ else 0 for _ in XOR_list])
DATA = np.array(data_list)
X_r2 = lda.fit(DATA, targs).transform(DATA)
plt.figure()
for c, i, target_name in zip("rg", [1, 0],["Cleaved","Not Cleaved"]):
plt.scatter(X_r2[targs == i], X_r2[targs == i], c=c, label=target_name)
plt.legend()
plt.title('LDA of cleavage_site dataset')
However, the LDA is only giving a 1D result
In: print X_r2[:5]
Out: [[ 6.74369996]
[ 4.14254941]
[ 5.19537896]
[ 7.00884032]
[ 3.54707676]]
However, the pca analysis will give 2 dimensions with the data I am inputting:
pca = PCA(n_components=2)
X_r = pca.fit(DATA).transform(DATA)
print X_r[:5]
Out: [[ 0.05474151 0.38401203]
[ 0.39244191 0.74113729]
[-0.56785236 -0.30109694]
[-0.55633116 -0.30267444]
[ 0.41311866 -0.25501662]]
edit: here is a link to two google-docs with the input data. I am not using the sequence information, just the numerical information that follows. The files are split up between positive and negative control data.
Input data:
file1
file2
LDA is not a dimensionality reduction technique. LDA is a classifier, the fact that people visualize decision function is just a side effect, and - unfortunately for your use case - decision function for binary problem (2 classes) is 1 dimensional. There is nothing wrong with your code, this is how every single decision function of a linear binary classifier looks like.
In general for 2 classes you get at most 1-dim projection and for K>2 classes you can get up to K-dim projection. With other decomposition techniques (like 1 vs 1) you can go up to K(K-1)/2 but again, only for more than 2 classes.
I'm working on implementing an interface between a TensorFlow basic LSTM that's already been trained and a javascript version that can be run in the browser. The problem is that in all of the literature that I've read LSTMs are modeled as mini-networks (using only connections, nodes and gates) and TensorFlow seems to have a lot more going on.
The two questions that I have are:
Can the TensorFlow model be easily translated into a more conventional neural network structure?
Is there a practical way to map the trainable variables that TensorFlow gives you to this structure?
I can get the 'trainable variables' out of TensorFlow, the issue is that they appear to only have one value for bias per LSTM node, where most of the models I've seen would include several biases for the memory cell, the inputs and the output.
Internally, the LSTMCell class stores the LSTM weights as a one big matrix instead of 8 smaller ones for efficiency purposes. It is quite easy to divide it horizontally and vertically to get to the more conventional representation. However, it might be easier and more efficient if your library does the similar optimization.
Here is the relevant piece of code of the BasicLSTMCell:
concat = linear([inputs, h], 4 * self._num_units, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(1, 4, concat)
The linear function does the matrix multiplication to transform the concatenated input and the previous h state into 4 matrices of [batch_size, self._num_units] shape. The linear transformation uses a single matrix and bias variables that you're referring to in the question. The result is then split into different gates used by the LSTM transformation.
If you'd like to explicitly get the transformations for each gate, you can split that matrix and bias into 4 blocks. It is also quite easy to implement it from scratch using 4 or 8 linear transformations.
I am using jlibsvm to do SVM for regression .My data set is very small (42 samples) . When I use the dataset to create the model using epsilon SVR with sigmoid kernel then no support vectors are generated.
This is what I get in my model file :
svm_type epsilon_svr
kernel_type sigmoid
gamma 0.02380952425301075
coef0 0.0
label
rho -66.42803
total_sv 0
probA -1.0
SV
When I use some other data set on the libsvm website I get a model file with support vectors fine.
Can someone please suggest why no support vectors are being generated for my data set ?
My data set file is formatted right so no issues there...
This could mean that the best found classification, given your data and the hyperparameters, is to assign the same label to all samples.
Are your samples unbalanced? What's the number of positive and negative samples? You might want to try to add a weighting to positive/negative samples to account for that
It could also be the samples are hard to separate given their structure and the kernel type. Have you tried a different structure?
With only 42 data samples, maybe you could add them to your question and get better answers.