This paper proposes an approach for segmenting a Web page into its semantic parts. Such analysis may be useful for adapting blog or other pages on small devices. In this approach, we take advantage of both dynamic layout after rendering and textual information. Our method segments the page into blocks and then classifies the blocks. A classification in semantic parts is performed thanks to a SVM-based machine learning approach using a set of 30 textual and visual-based features. Evaluation is conducted on a Web blog database. Results are provided for both block classification and blog segmentation into articles.

Multimedia applications are challenging software and require collaboration of very different components such as networking, rendering, or scripting to provide a nice user experience. GPAC is an open source multimedia framework that implements a vast number of components and helps experimenting with different types of multimedia applications. It provides a multimedia packager, some servers and a player for dynamic and interactive multimedia content. In this paper, we present the recent additions to the project which allow experimenting with new applications such as dynamic home networking or rich broadcasting of interactive content on the latest devices, including with 3D displays. Such experiments can be useful in research work and, as illustrated, in academic environment for educational purposes.

Our research focuses in a multimodal model of services for the dynamic composition of interactive features in multimedia. These services primarily do complex multimedia compositions in pervasive semantics systems. We propose a new protocol to enhance UPnP [14] and Bonjour [15] device descriptions with semantic multimodal descriptions and to extend the service discovery to high-level SOA services. This protocol bases on the upper-level ontology DOLCE+DnS Ultralite in conjunction with OWL-S and provides a flexible mean for multimodal rich media composition and context-aware presentation in a SOA architecture based on W3C's Multimodal interaction framework (MMIF).

The Scalable Vector Graphics (SVG) language allows in its version 1.2 the description of multimedia scenes including audio, video, vector graphics, interactivity and animations. This standard has been selected by the mobile industry as the format for vector graphics and rich media content. For this purpose, additional tools were introduced in the language to solve the problem of the playback of long-running SVG sequences on memory-constrained devices like mobile phones. However, the proposed tools are not entirely sufficient and solutions outside the scope of SVG are needed.

GPAC is a multimedia framework for research and academic purposes in different aspects of multimedia, with a focus on presentation technologies (graphics, animation and interactivity). The project started in 2003 with the initial goal to develop from scratch, in ANSI C, clean software compliant to the MPEG-4 Systems standard, a small and flexible alternative to the MPEG-4 reference software. Since then, the project has evolved into an advanced multimedia player, a multimedia packager and several servers. The project is intended to a wide audience ranging from end-users or content creators with development skills who want to experiment the new standards for interactive technologies or want to convert files for mobile devices, to developers who need players and/or server for multimedia streaming applications.

Our work focuses on the encoding of two-dimensional (2-D) graphics animated cartoons with MPEG-4. This stems from a need to stop encoding cartoons as videos and to start using methods more adapted to animated 2-D vector graphics sequences. We expected this could be an efficient coding with good versatility for various types of terminals. First, we present some technical specification for such cartoons. Next, we give an overview of MPEG-4 BIFS encoding methods. Then, we analyze how to optimize the use of these methods; some of our proposals could be useful for 2-D graphics applications other than animated cartoon. Last, we show some results with significantly better encoding efficiency over simple BIFS encoding.