A multi-view intelligent editor for digital video libraries
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Automatic Text Extraction from Video for Content-Based Annotation and Retrieval
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
On Using Partial Supervision for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Human performance measures for video retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Atomic topical segments detection for instructional videos
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MAGICAL demonstration: system for automated metadata generation for instructional content
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Effects of audio and visual surrogates for making sense of digital video
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatically generating high quality metadata by analyzing the document code of common file types
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Multimedia surrogates for video gisting: Toward combining spoken words and imagery
Information Processing and Management: an International Journal
Proceedings of the 19th international conference on World wide web
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This paper presents our latest work on building a system called MAGIC (Metadata Automated Generation for Instructional Content) that will automatically identify segments and generate critical metadata conforming with the SCORM (Sharable Content Object Reference Model) standard for instructional content. Various content analytics engines are utilized to automatically generate key metadata, which include audiovisual analysis modules that recognize semantic sound categories and identify narrators and informative text segments; text analysis modules that extract title, keywords and summary from text documents; and a text categorizer that classifies a document according to a pre-generated taxonomy. With MAGIC, instructional content developers can generate and edit SCORM metadata to richly describe their content asset for use in distributed learning applications. Experimental results obtained from collections of real data from targeted user communities will be presented.