Can a machine have two systems for recognition, like human beings?

Artificial Intelligence has attracted much of researchers' attention in recent years. A question we always ask is: “Can machines replace human beings to some extent?” This paper aims to explore the knowledge learning for an image-annotation framework, which is an easy task for humans but a tough task for machines. This paper's research is based on an assumption that machines have two systems of thinking, each of which handles the labels of images at different abstract levels. Based on this, a new hierarchical model for image annotation is introduced. We explore not only the relationships between the labels and the features used, but also the relationships between labels. More specifically, we divide labels into several hierarchies for efficient and accurate labeling, which are constructed using our Associative Memory Sharing method, proposed in this paper.