title: Limiting/controlling the number of hog features generated by scikit-image
body: <p>Following up from <a href="https://stackoverflow.com/questions/23008447/classification-test-in-scikit-learn-valueerror-setting-an-array-element-with-a/23015393?noredirect=1#comment35156661_23015393">this question</a>, I'm able to get the HoG features of an image into a numpy array that can be used for classification, but the number of HoG features of each image are different.</p>
  
  <p>So for example, one image produces an array of HoG features that has 2080 elements, and another an array of 1744, and so on.</p>
  
  <p>How can the number of HoG features generated be controlled? With <em>m</em> samples, I need to pass on an array of size <em>m x n</em> to a machine learning algorithm, so <em>n</em> needs to be constant. What I'm surprised about is that I've already resized all the images to the same size before extracting their HoG features, then why the difference?</p>