Multimedia content analysis and indexing: Evaluation of a distributed and scalable architecture

Multimedia search engines facilitate the retrieval of documents from large media content archives now available via intranets and the Internet. Over the past several years, many research projects have focused on algorithms for analyzing and indexing media content efficiently. However, special system architectures are required to process large amounts of content from real-time feeds or existing archives. Possible solutions include dedicated distributed architectures for analyzing content rapidly and for making it searchable. The system architecture we propose implements such an approach: a highly distributed and reconfigurable batch media content analyzer that can process media streams and static media repositories. Our distributed media analysis application handles media acquisition, content processing, and document indexing. This collection of modules is orchestrated by a task flow management component, exploiting data and pipeline parallelism in the application. A scheduler manages load balancing and prioritizes the different tasks. Workers implement application-specific modules that can be deployed on an arbitrary number of nodes running different operating systems. Each application module is exposed as a web service, implemented with industry-standard interoperable middleware components such as Microsoft ASP.NET and Sun J2EE. Our system architecture is the next generation system for the multimedia indexing application demonstrated by www.speechbot.com. It can process large volumes of audio recordings with minimal support and maintenance, while running on low-cost commodity hardware. The system has been evaluated on a server farm running concurrent content analysis processes.