A weakly supervised approach for semantic image indexing and retrieval

Abstract

This paper presents a new approach for building semantic image indexing and retrieval systems. Our approach is composed of four phases: (1) knowledge acquisition, (2) weakly-supervised learning, (3) indexing and (4) retrieval. Phase 1 is driven by a visual concept ontology which helps the expert to define low-level features useful to characterize object classes. Phase 2 uses acquired knowledge and image samples to learn the mapping between image data and visual concepts. Image indexing phase (phase 3) is fully automatic and produces semantic annotations of the images to index. The symbolic nature of querying enables userfriendly and fast retrieval (phase 4). We have applied our approach to the domain of transport vehicles (i.e. motorbikes, aircrafts, cars). © Springer-Verlag Berlin Heidelberg 2005.