MATLAB: One-Class classifier using Neural Network

background estimationDeep Learning Toolboxneural network

Hi all. I'm having a problem setting up a proper Neural Network for one class classification. Basically I've only the features that rapresent a background of an image. So the training phase would train the NN on those features. During the execution phase the NN will have features that could be "background" or "foreground" (the upper step is segmentation, I've already done it). How I'm supposed to set up correctly my NN?
Here is some piece of code:
toTrainFeat = computeFeatures(backBboxes,frame);
classes(1:size(backBboxes,1))=1;             % one-class
[net Y E] = adapt(net,toTrainFeat,classes);  % Incremental learning
if numFrame >=40 || sse(E) <0.01             %classify only after 40 frames OR if NN is smart enough
  y = net(toClassifyFeat);
  y
end
This code does not work, I think because I'm submitting only ONE-CLASS to the adapt method (in fact it crashes when it call adapt). Any help? Thanks a lot.

Best Answer

Use an RBF or EBF net. Generate points in between and outside of the clusters obtained from the original data.
Train the net with original data (1) and the simulated data outside and between clusters(0).
Hope this helps.
Thank you for formally accepting my answer
Greg
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