Image Retrieval as Linguistic and Nonlinguistic Visual Model Matching.
P. Bryan Heidorn · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 1999
THISARTICLE REVIEWS RESEARCH ON HOW people use mental models of images in an information retrieval environment. An understanding of these cognitive processes can aid a researcher in designing new systems and help librarians select systems that best serve their patrons. There are traditionally two main approaches to image indexing: concept-based and content-based (Rasmussen, 1997). The concept-based approach is used in many production library systems, while the content-based approach is dominant in research and in some newer systems. In the past, contentbased indexing supported the identification of “low-level” features in an image. These features frequently do not require verbal labels. In many cases, current computer technology can create these indexes. Conceptbased indexing, on the other hand, is a primarily verbal and abstract identification of “high-level” concepts in an image. This type of indexing requires the recognition of meaning and is primarily performed by humans. Most production-level library systems rely on concept-based indexing using keywords. Manual keyword indexing is, however, expensive and introduces problems with consistency. Recent advances have made some content-based indexing practical. In addition, some researchers are working on machine vision and pattern recognition techniques that blur the line between concept-based and content-based indexing. It is now possible to produce computer systems that allow users to search simultaneously on aspects of both concept-based and content-based indexes. The intelli