Building an Automatic Annotate Image System by using Bootstrapping

Y.-L. Lin, C.-H. Kuo, and N.-L. Tsao (Taipei)


Bootstrapping, Image Retrieval, Semantic Analysis, and Distance Learning


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.

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