title: Image classification - adding new classes to existing model
body: <p>I am using the classical SIFT - BOW - SVM for image classification. My classifiers are created using the 1vsAll paradigm.</p>
  
  <p>Let's say that I currently have 100 classes.
  Later, I would like to add new classes OR I would like to improve the recognition for some specific classes using additional training sets.</p>
  
  <p>What would be the best approach to do it ?
  Of course, the best way would be to re-execute every steps of the training stage. 
  But would it make any sense to only compute the additional (or modified) classes using <strong>the same vocabulary as the previous model</strong>, in order avoid to recompute a new vocabulary and train again ALL the classes ?</p>
  
  <p>Thanks</p>