Image retrieval and semiautomatic annotation scheme for large image databases on the Web

Image annotation is used in traditional image database systems. However, without the help of human beings, it is very difficult to extract the semantic content of an image automatically. On the other hand, it is a tedious work to annotate images in large databases one by one manually. In this paper, we present a web based semi-automatic annotation and image retrieval scheme, which integrates image search and image annotation seamlessly and effectively. In this scheme, we use both low-level features and high-level semantics to measure similarity between images in an image database. A relevance feedback process at both levels is used to refine similarity assessment. The annotation process is activated when the user provides feedback on the retrieved images. With the help of the proposed similarity metrics and relevance feedback approach at these two levels, the system can find out those images that are relevant to the user's keyword or image query more efficiently. Experimental results have proved that our scheme is effective and efficient and can be used in large image databases for image annotation and retrieval.