Abstract

Online TV services have facilitated time-shifted TV viewing. Accordingly, new service concepts are needed to improve access to relevant information in the broadcast TV content. In this paper we introduce a system that indexes TV broadcast in near real-time from seven free-to-air television channels. Our system uses machine learning and data mining techniques to extract descriptive novelty word summaries and picture highlights automatically from TV program subtitles and uses them to provide non-linear content-based access to relevant TV content fragments in novel end-user services and applications. First end-user service allows browsing of recent time shifted TV content using time and program genre metaphors with extracted program summaries. Second end-user service provides free-text search of archival and time shifted TV content. Additionally, the system allows content-based recommendation of similar TV content from the database of 180 000 indexed programs. Recommendations are used in the enduser services and Mobile EPG Guide application. Over 5 000 user sessions have been collected to study how users adopt our content-based access metaphors to examine interesting TV content. User logs revealed that the proposed contentbased access techniques were more popular in TV program search and browsing activities than conventional techniques based on program title and description metadata.

Full Text

Paper version not known (Free)

Published Version

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.