In the object recognition community, much effort has been spent on devising expressive
object representations and powerful learning strategies for designing effective classifiers,
capable of achieving high accuracy and generalization. In this scenario, the focus on the
training sets has been historically weak; by and large, training sets have been generated
with a substantial human intervention, requiring considerable time. In this paper, we present
a strategy for automatic training set generation. The strategy uses semantic knowledge …