I need some advice. I am currently trying to do a printer classification with ML/DL.
What do I have?
11 colored-images with high resolution from 8 different inkjet-printers (in total 88 images) I have 8 classes (printers) All images are scanned with 2.400 dpi, so you are able to see the halftone of the images and the matrix dots I know each printers are different in terms of size of matrix dots, dot pattern etc. Based on that I need to do a feature extraction and train a ML model which can classify the correct printer. There is a previous work which has been done with Wavelet-Transformation for feature extraction and SVM for classification. The goal now is to find another approach of feature extraction and training.
My question here is, what do you think is the best solution?
My idea is:
Isolate the dots into binary color (black/white) do an edge detection with opencv (using filters like sobel, canny etc.) But I am not sure if this is a good approach. After reading a lot of papers on related work I found out many used Transfer Learning (e.g. VGG, Resnet) where features are learned in the training process.
So basically I have images and when you can zoom in you see for each printers the pattern are different. So instead of doing Wavelet-Transformation I need to do another approach.
In the litarature common feature extractor for this are Gray-levcel Co-Occurences, Wavelet-Transformation, Spartial filters which will be used in SVM or AdaBoost. Another approach is as said above with pre-trained CNN (transfer learning).
So, what do you think I should tackle next?