KUUKKELI-TV: ONLINE CONTENT-BASED SERVICES AND APPLICATIONS FOR BROADCAST TVWITH LONG-TERM USER EXPERIMENTS

Online TV services have facilitated time-shifted TV viewing. Accordingly, new service concepts are needed to improve ac-cess 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 applica-tions. 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 con-tent. Additionally, the system allows content-based recom-mendation of similar TV content from the database of 180 000 indexed programs. Recommendations are used in the end-user 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 content-based access techniques were more popular in TV program search and browsing activities than conventional techniques based on program title and description metadata. Index Terms — content-based analysis, natural language processing, video data mining, semantic video summariza-tion, DVB broadcasting, catch-up TV 1