An Integrative Semantic Framework for Image Annotation and Retrieval

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 366 - 373
• Main Authors: Osman, Taha, Thakker, Dhavalkumar, Schaefer, Gerald, Lakin, Phil
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Semantic networks; Information systems > Information retrieval; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information systems applications > Multimedia information systems > Multimedia databases
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.17

Most public image retrieval engines utilise free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. Our semantic retrieval technology is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. We also present our efforts in further improving the recall of our retrieval technology by deploying an efficient query expansion technique.