The performance of a content-based information retrieval (CBIR) system is very subjective and hence user-dependent. To the user, similarity between objects in the database is often highlevel and semantic. However, features extracted from objects directly in their digital representations are often low-level features. The gap between low-level features and high-level semantics has been the major obstacle to better retrieval performance.

In this talk we will outline several approaches to bridging the gap between low-level features and high-level semantics, including hidden annotation and relevance feedback. We will present a few specific techniques: active learning, annotation propagation, feature space warping, and semantic metric linking, all aiming at propagating the semantics from some objects to the others.