Semantic Constraint Based Target Object Recognition

Aiming at the problems above, we proposed one semantic constraint based object recognition method. On the one hand, instance-based transfer learning model could make use of learning instances of other categories to maintain realistic recognition accuracy. On the other hand, semantic constraint between different regions simulated as joint entropy is used to recognize target object more accurately. At last, adequate experiments using a large number of images show that our model not only could reduce the number of learning instances but also could achieve realistic recognition.