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Séminaire de

Mark Zhang

10 juin 2010
Amphithéâtre de l'IRCICA

Enhanced Max-Margin Learning on Multimodal Data Mining in a Multimedia Database

The problem of multimodal data mining in a multimedia database can be addressed as a structured prediction problem where we learn the mapping from an input to the structured and interdependent variables. In this talk, I will introduce a new max-margin learning approach called Enhanced Max-Margin Learning (EMML) framework. In addition, I will show that the EMML framework can be applied as an effective solution to the multimodal data mining problem in a multimedia database. I will show that EMML is much more efficient in learning with a much faster convergence rate, which is guaranteed through theoretical analysis and is also verified in empirical evaluations. I will also show that EMML as an effective solution to the multimodal data mining problem that is highly scalable in the sense that the query response time is independent of the database scale, allowing facilitating a multimodal data mining querying to a very large scale multimedia database, and excelling many existing multimodal data mining methods in the literature that do not scale up at all. To showcase the strength of EMML, we apply it to the Berkeley Drosophila embryo image database and a Web image collection, and I will report the performance comparison with a state-of-the-art multimodal data mining method.

This is a joint work with Zhen Guo of SUNY Binghamton, and Eric P. Xing and Christos Faloutsos of CMU.

Ours

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