Automatic metadata extraction and indexing for reusing e-learning multimedia objects

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In this paper we present the architecture of a Digital Library for enabling the reusing of audiovisual documents in an e-Learning context. The reuse of Learning Objects is based on automatically extracted descriptors carrying a semantic meaning for the professional that uses these Learning Objects to prepare new interactive multimedia lectures. The presented system is based on MILOS, a general purpose Multimedia Content Management System created to support design and effective implementation of digital library applications. MILOS supports the storage andcontent based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in XML. The objective is to demonstrate the reuse of digital content, as video documents or Power Point presentations, by exploiting existing technologies for automatic extraction of metadata (OCR, speech recognition, cut detection, MPEG-7 visual descriptors, etc.). The search interface assists the user of the system in the retrieval the multimedia objects in the collection, by combining full-text retrieval on text extracted and metadata, and similarity search on the MPEG-7 visual descriptors. Copyright 2007 ACM.




Bolettieri, P., Falchi, F., Gennaro, C., & Rabitti, F. (2007). Automatic metadata extraction and indexing for reusing e-learning multimedia objects. In Proceedings of the ACM International Multimedia Conference and Exhibition (pp. 21–28).

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