title: How to use SIFT features/descriptors as input for SVM training?
body: <p>I want to classify MRI images of a brain tumor into benign and malignant using C++. I am using SIFT features and the paper I am following clustered them using kmeans before training the SVM classifier. What I don't understand is why is there a need to do that? From what I know, kmeans only clusters the features; it doesn't change the size of the input.</p>
  
  <p>I have read that possible ways are BoW and histogram. <strong>In the histogram approach</strong>, It just counts the # of features in each cluster right? I don't think that will provide the information I'll need for classifying benign and malignant tumors because they can be both small and big. <strong>In BoW approach</strong>, I didn't understand this <a href="https://stackoverflow.com/questions/30928135/how-to-use-sift-features-for-bag-of-words-in-opencv">link</a>.</p>
  
  <p>Basically, I don't know what to do with my SIFT features to use it as input for SVM. Do I really have to create a dictionary of some sort? I'm begging you, please enlighten me. Thank you very much!</p>