Semantic Multimedia Information: Mining, Fusion and Extraction
This event took place on Wednesday 14 February 2007 at 12:00
Joćo Magalhćes Imperial College London, and KMi, The Open University
The extraction of semantic information from multimedia content is a research area that faces multiple challenges: scalability; data scarcity; multiple statistical models for each modality; computational limitations when processing large-scale training datasets; incorrect ground truth...
To address some of the issues hindering multimedia retrieval applications we propose a novel learning framework to extract semantic multimedia information. The framework combines both knowledge and statistical data, and it is divided in three parts: (1) multimedia mining, (2) multi-modal information fusion, and (3) semantic information extraction.
We will discuss several aspects of the framework, such as, scalability, its solid statistical foundation (borrowed from Generalized Linear Models and Bayesian Theory), how it is able to elegantly cope with different modalities, and its performance on semantic image retrieval and large-scale semantic video retrieval.
This event took place on Wednesday 14 February 2007 at 12:00
The extraction of semantic information from multimedia content is a research area that faces multiple challenges: scalability; data scarcity; multiple statistical models for each modality; computational limitations when processing large-scale training datasets; incorrect ground truth...
To address some of the issues hindering multimedia retrieval applications we propose a novel learning framework to extract semantic multimedia information. The framework combines both knowledge and statistical data, and it is divided in three parts: (1) multimedia mining, (2) multi-modal information fusion, and (3) semantic information extraction.
We will discuss several aspects of the framework, such as, scalability, its solid statistical foundation (borrowed from Generalized Linear Models and Bayesian Theory), how it is able to elegantly cope with different modalities, and its performance on semantic image retrieval and large-scale semantic video retrieval.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Social Software is...
Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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