Current IPTV monetization focuses on mainstream programming that is well understood, with metadata that is stored in advance. Current contextual advertising is focused on web searches and static information. As the demand for online internet video grows into the IPTV market, the need to monetize it effectively becomes important. As the advertising market for IPTV expands into less mainstream video (e.g. YouTube and other user generated content) it becomes more important to automate the generation of metadata for the video The metadata associated with self-described YouTube videos are unreliable and can not be used as a basis for effective contextual advertising. Automatic generation, however, brings down the cost of creating metadata and gives advertisers a neutral party that evaluates the data. We propose a system in which a trusted intermediary automatically generates reliable metadata for multimedia.

We propose an approach to searching and browsing video and multimedia using the results of face clustering. Once a set of faces has been detected we use various techniques to create a set of clusters of faces from a particular video. That set of clusters encourages new approaches to browsing and searching video that are not linguistic and can apply to videos internationally, in any language.

IPTV customers will have access to thousands of video content sources and will require powerful yet intuitive tools to locate desired content. We propose a solution based on stored user interest profiles and multimodal

This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While in its simplest form, one can extend text data mining to speech data mining by using text tools on the output of a speech recognizer, we have found that it is not optimal. We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances.

We present a system for automatic monitoring and timely dissemination of multimedia information to a range or mobile information appliances based on each user's interest profile. Multimedia processing algorithms detect and isolate relevant video segments from over twenty television broadcast programs based on a collection or words and phrases specified by the user. Content repurposing techniques are then used to convert the information into a form that is suitable for delivery to the user's mobile devices. Alerts are sent using a number of application messaging and network access protocols including email, short message service (SMS), multimedia messaging service (MMS), voice, session initiation protocol (SIP), fax, and pager protocols. The system is evaluated with respect to performance and user experiences. The MediaAlert system provides an effective and low-cost solution for the timely generation of alerts containing personal, business, and security information.

This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While, in its simplest form, one can extend text data mining to speech data mining by using text tools on the output of a speech recognizer, we have found that it is not optimal. We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances. For the purposes of this paper, we examine semantic data mining in the context of semantic parsing and analysis in a specific situation involving the solution of a business problem that is known to the analyst. We are not attempting a generic semantic analysis of a set of speech. Our tools and methods allow the analyst to mine the speech data to discover the semantics that best cover the desired solution. The coverage, in this case, yields a set of Natural Language Understanding (NLU) classifiers that serve as testable hypotheses.