Hi,
I am trying to classify (distinguish) between several different swimming fish who are very small, look very similar, and are very similar in size. Please note, I have already segmented the objects from the background and now need to associate them to the objects in consecutive frames. I have been trying to distinguish each object by comparing their spatial coordinates and sizes between consecutive frames and this works pretty well but is sensitive to noise (gaps in frame detection where the objects are intersecting and can't be properly segmented, etc.), so I need help with the following:
1. Should I experiment with some sort of probabilistic classifier (Normal bayes, EM, etc.) or a more discriminative one such as Boosting, random trees, or even SVM? I'm not sure which one to use with my current problem.
2. Which would be the best features to use for training the classifier? I have access to each blob's properties such as their pixel content, contour area, centroid location, etc.
Any suggestions??
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