BRIDGING THE SEMANTIC GAP: EXPLORING DESCRIPTIVE VOCABULARY FOR IMAGE STRUCTURE
Caroline Beebe · Advances in Classification Research Online · 2007
This research makes a methodological contribution to the development of faceted vocabularies and suggests a potentially significant tool for the development of more effective image retrieval systems.The research project applied an innovative experimental methodology to collect terms used by subjects in the description of images drawn from three domains.The resulting natural language vocabulary was then analyzed to identify a set of concepts that were shared across subjects.These concepts were subsequently organized as a faceted vocabulary that can be used to describe the shapes and relationships between shapes that constitute the internal spatial composition --or internal contextuality --of images.Because the vocabulary minimizes the terminological confusion surrounding the representation of the content and internal composition of digital images in Content-Based Image Retrieval [CBIR] systems, it can be applied to develop more effective image retrieval metrics and to enhance the selection of criteria for similarity judgments for CBIR systems.CBIR is a technology made possible by the binary nature of the computer.Although CBIR is used for the representation and retrieval of digital images, these systems make no attempt either to establish a basis for similarity judgments generated by query-by-pictorial-example searches or to address the connection between image content and its internal spatial composition.The disconnect between physical data (the binary code of the computer) and its conceptual interpretation (the intellectual code of the searcher) is known as the semantic gap.A descriptive vocabulary capable of representing the internal visual structure of images has the potential to bridge this gap by connecting physical data with its conceptual interpretation.Beebe, C. (2007).Bridging the semantic gap: Exploring descriptive vocabulary for image structure.