Building an Image Database for Teaching and Learning by Using Bootstrapping
Yun-Long Lin, Chin-Hwa Kuo, Nai-Lung Tsao, Tamkang University, Taiwan
EdMedia + Innovate Learning, in Vancouver, Canada ISBN 978-1-880094-62-4 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
The images are always playing an important role on teaching and learning. However, it is not easy to get sufficient and appropriate images rapidly for these purposes. In this paper, we have designed a database that can automatically collect and classify images; This database is characterized by high level features to image classifying. Its features include: extending a keyword through bootstrapping construction. Aside from using bootstrapping construction to expand keywords and to classify images, we have also added a discriminative feature metric to increase the precision and recall rates of image classifying to our standards. Finally, we apply this technique on an image archives management system, named "Campus Image System". Instructors and students can get appropriate images via this system, for supporting their teaching or learning purposes.
Lin, Y.L., Kuo, C.H. & Tsao, N.L. (2007). Building an Image Database for Teaching and Learning by Using Bootstrapping. In C. Montgomerie & J. Seale (Eds.), Proceedings of ED-MEDIA 2007--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 1481-1487). Vancouver, Canada: Association for the Advancement of Computing in Education (AACE).
© 2007 Association for the Advancement of Computing in Education (AACE)