Recently I did some Support Vector Machine (SVM) tests in R (statistical language with functional parts for rapid prototyping and data analysis — somehow similar to Matlab, but open source 
) for my current face recognition projects. To get my SVMs up and running in R, using image data as in- and output, I wrote a small demo script for classifying images. As test data I used 2 classes of images (lines from left top to right bottom and lines from left bottom to right top), with 10 samples each — like these:
The complete image set is available here.
For SVM classification simple train and test sets get used — for more sophisticated problems n-fold cross validation for searching good parameter settings is recommended instead. For everybody who did not yet work with SVMs, I’d recommend reading something about how to start with “good” SVM classification, like the pretty short and easy to read “A Practical Guide to Support Vector Classification” from Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin (the LIBSVM-inventors).
Update: added parallel processing using parallel and mclapply for loading image data (for purpose of demonstration only, loading 10 images in parallel does not make a big difference ).
print('starting svm demo...')
library('png')
library('e1071')
library('parallel')
# load img data
folder<-'.'
file_list <- dir(folder, pattern="png")
data <- mclapply(file_list, readPNG, mc.cores=2)
# extract subject id + img nr from names
subject_ids <- lapply(file_list, function(file_name) as.numeric(unlist(strsplit(file_name, "_"))[1]))
# rename subject id's to c1 and c2 for more clear displaying of results
subject_ids[subject_ids==0]='c1'
subject_ids[subject_ids!='c1']='c2'
img_ids <- lapply(file_list, function(file_name) as.numeric(unlist(strsplit(unlist(strsplit(file_name, "_"))[2], "\\."))[1]))
# specify which data should be used as test and train by the img nrs
train_test_border <- 7
# split data into train and test, and bring into array form to feed to svm
train_in <- t(array(unlist(data[img_ids < train_test_border]), dim=c(length(unlist(data[1])),sum(img_ids < train_test_border))))
train_out <- unlist(subject_ids[img_ids < train_test_border])
test_in <- t(array(unlist(data[img_ids >= train_test_border]), dim=c(length(unlist(data[1])),sum(img_ids >= train_test_border))))
test_out <- unlist(subject_ids[img_ids >= train_test_border])
# train svm - try out different kernels + settings here
svm_model <- svm(train_in, train_out, type='C', kernel='linear')
# evaluate svm
p <- predict(svm_model, train_in)
print(p)
print(table(p, train_out))
p <- predict(svm_model, test_in)
print(p)
print(table(p, test_out))
print('svm demo done!')
