Addressing the ImageClef 2009 challenge using a patch-based visual words representation

Abstract

This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.