A fast automatic image annotation based on the mutual K adjacency graph

Image semantic has the characters of vague and abstractive, therefore, only low-level feature is not enough to describe image semantics. It requires to combine with image related content in order to improve the accuracy of the image annotation In this paper, An image annotation algorithm based on mutual K adjacency graph is proposed which describes the relationship between the low-level features, annotated words and image. Semantic information is extracted from paired-nodes in mutual K adjacency graph, and improves effectively the image annotation performance. Based on the analysis on the structure of mutual K adjacency graph, Combined with Random Walk with Restart (RWR), A fast algorithm, without apparent reducing the precision of the image annotation, is proposed.