Foundations of statistical natural language processing
Foundations of statistical natural language processing
Document clustering with cluster refinement and model selection capabilities
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
| Hi-index | 0.00 |
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.