Bridging the semantic gap: Exploring descriptive vocabulary for image structure

Content-Based Image Retrieval (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. The research project addressed three questions: Is there a shared vocabulary of terms used by subjects to represent the internal contextuality (i.e., composition) of images? Can the natural language terms be organized into concepts? And, if there is a vocabulary of concepts, is it shared across subject pairs? A natural language vocabulary was identified on the basis of term occurrence in oral descriptions provided by 21 pairs of subjects participating in a referential communication task. In this experiment, each subject pair generated oral descriptions for 14 of 182 images drawn from the domains of abstract art, satellite imagery and photo-microscopy. Analysis of the natural language vocabulary identified a set of 1,319 unique terms which were collapsed into 545 concepts. These terms and concepts were organized into a faceted vocabulary. This faceted vocabulary can contribute to the development of more effective image retrieval metrics and interfaces to minimize the terminological confusion and conceptual overlap that currently exists in most CBIR systems. For both the user and the system, the concepts in the faceted vocabulary can be used to represent shapes and relationships between shapes (i.e., internal contextuality) that constitute the internal spatial composition of an image. Representation of internal contextuality would contribute to more effective image search and retrieval by facilitating the construction of more precise feature queries by the user as well as the selection of criteria for similarity judgments in CBIR applications.