Relevance Feedback in Content-Based Image and Video Retrieval

Thomas Huang

Department of Electrical and Computer Engineering
Beckman Institute, University of Illinois at Urbana-Champaign

Montag, 15.07.2002, 16 Uhr c.t., Hörsaal 9
Compared to text databases, image and video databases are relatively newcomers. They offer new possibilities and new challenges. In particular, for images and video, it is possible to query by example and similarity in low-level features (color, texture, shape/structure, motion, and audio features in the case of video). We shall present algorithms for improving the performance of retrieval by relevance feedback from the user. Our approach is to consider the problem as two-category classification: Relevant and irrelevant classes. We shall also discuss: The use of unlabeled data in designing the classification algorithm; and the combined use of low-level feathres and keywords in querying. Some experimental results will be shown.

sfb-logo Zur Startseite Erstellt von: Anke Weinberger (2002-05-21).
Wartung durch: Anke Weinberger (2002-05-21).