Heidorn, P.B.: Image retrieval as linguistic and nonlinguistic visual model matching (1999)
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- Abstract
- This article 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, content-based 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. Concept-based 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 intelligent application of this technology requires an understanding of the user's visual mental models of images and cognitive behavior.
