Hierarchical Long-Term Learning for Automatic Image Annotation

This paper introduces a hierarchical process for propagating image annotations throughout a partially labelled database. Long-term learning, where users’ query and browsing patterns are retained over multiple sessions, is used to guide the propagation of keywords onto image regions based on low-level feature distances. We demonstrate how singular value decomposition (SVD), normally used with latent semantic analysis (LSA), can be used to reconstruct a noisy image-session matrix and associate images with query concepts. These associations facilitate hierarchical filtering where image regions are matched based on shared parent concepts. A simple distance-based ranking algorithm is then used to determine keywords associated with regions.