Multimedia Content-Based Indexing and Search: Challenges and Research Directions

Abstract

New digital multimedia content is being generated at a tremendous rate. At the same time, the growing variety of distributions channels, e.g., Web, wireless/mobile, cable, IPTV, satellite, is increasing users’ expectations for accessibility and searchability of digital multimedia content. However, users are still finding it difficult to find relevant content and indexing & search are not keeping up with the explosion of content. Recent advances in multimedia content analysis are helping to more effectively tag multimedia content to improve searching, retrieval, repurposing and delivering of relevant content. We are currently developing a system called Marvel that uses statistical machine learning techniques and semantic concept ontologies to model, index and search content using audio, speech and visual content. The benefit is a reduction in manual processing for tagging multimedia content and enhanced ability to unlock the value of large multimedia repositories.