Conference Paper

From Data To Insight: The Community Of Multimedia Agents.

DOI: 10.1007/978-3-540-39666-6_10 Conference: Proceedings of the Third International Workshop on Multimedia Data Mining, MDM/KDD'2002, July 23rd, 2002, Edmonton, Alberta, Canada
Source: DBLP


Multimedia Data Mining requires the ability to automatically analyze and understand the content. The Community of Multimedia Agents project (COMMA) is devoted to creating an open environment for developing, testing, learning and prototyping multimedia content analysis and annotation methods. It serves as a medium for researchers to contribute and share their achievements while protecting their proprietary techniques. Each method is represented as an agent that can communicate with the other agents registered in the environment using templates that are based on the Descriptors and Description Schemes in the emerging MPEG-7 standard. This allows agents developed by different organizations to operate and communicate with each other seamlessly regardless of their programming languages and internal architecture. A Development Environment is provided to facilitate the construction of media analysis methods. The tool contains a Workbench using which the user can integrate the agents to build more sophisticated systems, and a Blackboard Browser that visualizes the processing results. It enables researchers to compare the performance of different agents and combine them to build more powerful and robust system prototypes. The COMMA can also serve as a learning environment for researchers and students to acquire and test cutting edge multimedia analysis algorithms. Thus the efficiency of research in this area can be improved by sharing of media agents.

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Available from: Valery Petrushin, Oct 04, 2015
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    • "Academic researchers would usually be happy to get ready their tools for sharing if it does not take too much effort. This situation triggered the Community of Multimedia Agents project at the Accenture Technology Labs [1] "
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    ABSTRACT: This paper presents a methodology for developing audio processing agents for a multi-agent environment that is known as the Community of Multimedia Agents. The Community's philosophy, objectives and architecture are described. The methodology is illustrated using audio feature extraction agents as example. The algorithms used for extracting audio features are classical and work in general audio domain. The agents have the standard MPEG-7 interface for better interoperation and wide usage. Two low level tools -the MPEG-7 audio descriptor wrapper classes and the MPEG audio decoder – are also presented. An example of agent aggregation for an annotation system prototyping is provided.
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    ABSTRACT: The explosion of multimedia content in databases, broadcasts, streaming media, etc. has generated new requirements for more eective access to these global information repositories. Content extraction, indexing, and retrieval of multimedia data continues to be one of the most challenging and fastest-growing research areas. A consequence of the growing consumer demand for multimedia information is that sophisticated technology is needed for representing, model- ing, indexing, and retrieving multimedia data. In particular, we need robust techniques to index/retrieve and compress multimedia information, new scal- able browsing algorithms allowing access to very large multimedia databases, and semantic visual interfaces integrating the above components into unified multimedia browsing and retrieval systems. The aim of these systems is to handle general queries such as "find outdoor pictures or videos of an interview with James Cameron discussing the making of the Titanic film." Answering such queries requires intelligent exploitation of both speech and visual content. For multimedia retrieval, the combination of multiple integrated media types increases the performance of content-based retrieval. Available content analysis and retrieval techniques tailored to a spe- cific media are therefore not adequate for queries as the one mentioned above. Clearly, Multimedia Information Retrieval is a very broad area covering both structural issues (e.g. framework, storage, networking, client-server models) and intelligent content analysis and retrieval. These all need to be integrated into a seamless whole which involves expertise from a wide variety of fields.
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    ABSTRACT: ABSTRACT The aim of this ,paper is to present an overview ,of the ,current situation in this hot topic of Multimedia ,Information Retrieval: Personalization. We ,are ,considering ,several aspects of this problem. On one hand, the user will want to have a personalized access to his image/video collections and this can be achieved,by providing,intuitive and ,natural ,browsing ,capabilities and customized features. Furthermore, the system is required to perform user profiling and to adapt the existing parameters of the system to the user needs and interest. On the other hand, it is also important to consider ,the devices and applications in which ,this technology,is going ,to be ,deployed. Mobile media ,is a ,high growth,area but the state-of-the-art technologies ,are lagging behind,the consumers ,expectations. We are ,addressing ,in this paper precisely these three important aspects. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information Filtering; H.4 [Information Systems Applications]: Miscellaneous General Terms
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