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
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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