Alberto Messina

Multimodal Aggregation and Recommendation Technologies Applied to Informative Content Distribution and Retrieval

In the modern age, cross-media production is an innovative technique used by the media industry to ensure a positive return on investments while optimising productivity and market coverage. So that, technologies for the seamless fusion of heterogeneous data streams are increasingly considered important, and thus research efforts have started to explore this area. After having aggregated heterogeneous sources, what has to be addressed as the next problem is efficiency in retrieving and using the produced content. Tools for personalised and context-oriented multimedia retrieval are indispensable to access desired content from the aggregated data in a quicker and more useful way. This chapter describes the problems connected with this scenario and proposes an innovative technological framework to solve them, in the area of informative content (news) distribution and retrieval. Extensive experiments prove the effectiveness of this approach in a real-world business context.

The MPEG-7 Audiovisual Description Profile (AVDP) and its application to multi-view video

This paper describes a new MPEG-7 profile called AVDP (Audiovisual Description Profile). Firstly, some problems with conventional MPEG-7 profiles are described and the motivation behind the development of AVDP is explained based on requirements from broadcasters and other actors from the media industry. Secondly, the scope and functionalities of AVDP are described. Differences from the existing profiles and the basic AVDP structure and components are explained. Some useful software tools handling AVDP, including for validation and visualization are discussed. Finally the use of AVDP to represent multi-view and panoramic video content is described.

Content-Based Keywords Extraction and Automatic Advertisement Associations to Multimodal News Aggregations

Nowadays, Web is characterized by a growing availability of multimedia data together with a strong need for integrating different media and modalities of interaction. Hence, one of the main challenges is to bring into the Web data thought and produced for different media, such as TV or press content. In this scenario, we focus on multimodal news aggregation retrieval and fusion. Multimodality, here, is intended as the capability of processing, gathering, manipulating, and organizing data from multiple media. In particular, we tackle two main issues: to extract relevant keywords to news and news aggregations, and to automatically associate suitable advertisements to aggregated data. To achieve the first goal, we propose a solution based on the adoption of extraction-based text summarization techniques; whereas to achieve the second goal, we developed a contextual advertising system that works on multimodal aggregated data. To assess the proposed solutions, we performed experiments on Italian news aggregations. Results show that, in both cases, the proposed solution performs better than the adopted baseline solutions.

Personalized Semantic News: Combining Semantics and Television

The integration of semantic technologies and television services is an important innovation in traditional broadcasting in order to improve services delivered to end users in an extended home environment: new methods emerge for getting TV content via the Web and interacting with TV content on end users devices. This paper gives a short description of a Personalized Semantic News scenario in the context of the NoTube project illustrating the use of semantics for personalized filtering and access of news items.