Knowledge-Based Image Understanding Systems: A Survey

• Published in: Computer vision and image understanding Vol. 67; no. 2; pp. 161 - 185
• Main Authors: Crevier, Daniel, Lepage, Richard
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 01.08.1997; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Learning and adaptive systems; Pattern recognition. Digital image processing. Computational geometry; System architecture; Image recognition; Segmentation; Image processing; Image interpretation; Taxonomy; Expert system; Knowledge representation; Inference; Knowledge base; Learning; Programming language; User interface; Semantics; Production rule; Database; Artificial intelligence
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1006/cviu.1996.0520

The development of software that would be to image understanding systems what expert system shells are to expert systems has been the subject of considerable enquiry over the last ten years: this paper reviews pertinent publications and tries to present a coherent view of the field. After a survey of the advantages of explicit knowledge representation in image understanding, we tackle the subject under two main headings. We first expose the nature of the knowledge that the various authors have represented for image understanding. To this effect, we have elaborated a knowledge taxonomy consisting of seven modules, ranging in specificity from task domain knowledge to generic knowledge about the use of software systems. We then examine how researchers have represented these various kinds of knowledge. Most of the representations known to artificial intelligence were pressed into service, and a discussion of their relative merits is presented.