Take the 2-minute tour ×
Signal Processing Stack Exchange is a question and answer site for practitioners of the art and science of signal, image and video processing. It's 100% free, no registration required.

I have 75 images of handwritten signs from which I extracted 7 Hu moments and solidity features. How can I find similarities among them to train a classifier and predict the value? I thought SVM was a good choice, but I don't have a target vector (what do I put? I do not know differences in signs that I can say there are, say, three labels, such as "circle", "triangle" or "square").

Is cluster analysis more appropriate? If yes, What is best method? I am using the scikit-learn module in python.

http://scikit-learn.org/stable/modules/svm.html

http://scikit-learn.org/stable/modules/clustering.html

share|improve this question
    
    
Can you show us the full set of samples? –  Emre Feb 6 '13 at 21:02
    
add comment

1 Answer 1

It is a bit hard to understand what you are trying to do. What are these signs? The one you posted looks like a wheel. Are there meaningful categories that you can name? If so, then this is a supervised learning (classification) problem, and you should use a classification algorithm such as SVM.

If there are no clear labels, but you want to group together similar-looking signs, then this is an unsupervised learning problem, and you should use a clustering algorithm.

From your description it sounds more like a clustering problem.

share|improve this answer
    
    
I'm not sure if it is better affinity propagation mean shift or dbscan with my image set, and after i found clusters there is any function that tell me most similar images inside the cluster? –  postgres Feb 7 '13 at 16:01
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.