It soon appeared clear, however, that extracting robust, discriminative features was only half the job for recognition tasks. For instance, different elements from the same class can look quite different (such as different-looking dogs) and, as a result, share only a small set of common features. Therefore, unlike image-matching tasks, higher-level problems such as semantic classification cannot be solved by simply comparing pixel features from query images with those from labeled pictures (such a procedure can also become sub-optimal in terms of processing time if the comparison has to be done with every image from a large labeled dataset).
This is where machine learning come into play. With an increasing number of researchers trying to tackle image classification in the 90s, more statistical ways to discriminate images based on their features started to appear. Support vector machines (SVMs), which were standardized by Vladimir Vapnik and Corinna...
