Category-based search using metadatabase in image retrieval

We present a self-adjustable metadatabase aimed at improving the performance of the relevance feedback module extensively used in content-based image retrieval systems. Our metadatabase provides a mechanism for accumulating the optimized relevance feedback records (which are called metadata records) obtained from previous queries. Each metadata record in the metadatabase includes optimal query, feature weights, and identifiers of relevant and/or irrelevant images, and can be effectively used to guide future queries. With the metadatabase, the relevance feedback module admits a noticeable improvement on its performance for category-based search, especially when the relevant images form multiple classes in the feature space. Experiments on a Corel image set (with 31,438 images) show that our method has at least a 15% improvement on average precision and recall over relevance-feedback-only approaches.