VCSR: Video content summarization for recommendation

Chi Cheng Tsai, Ching I. Chung, Yi Ting Huang, Chia Hsing Shen, Yu Chieh Wu, Jie Chi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper, the authors present a Video Content Summarization for Recommendation (called VCSR) system to auto-recommend suitable multimedia learning materials for learners. The VCSR system firstly extracts important content as summarization from input raw video data, while the generated summarization will be auto-routed to learners according to their profiles. Video captions are initially recognized using Optical Character Recognition (OCR), then a set of key passages with corresponding frame images are extracted to form a video summary. The recommendation is achieved by calculating the relevance of the video summarization for each learner. Also, this paper indicates how the VCSR system effectively plays the intermediate role in a modern digital library.

Original languageEnglish
Title of host publicationProceedings - The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007
Pages862-864
Number of pages3
DOIs
StatePublished - 2007
Event7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007 - Niigata, Japan
Duration: 18 Jul 200720 Jul 2007

Publication series

NameProceedings - The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007

Conference

Conference7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007
Country/TerritoryJapan
CityNiigata
Period18/07/0720/07/07

Fingerprint

Dive into the research topics of 'VCSR: Video content summarization for recommendation'. Together they form a unique fingerprint.

Cite this