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

摘要

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 output variables. In this paper, built upon the existing literature on the max margin based learning, we develop a new max margin learning approach called Enhanced Max Margin Learning (EMML) framework. In addition, we apply EMML framework to developing an effective and efficient solution to the multimodal data mining problem in a multimedia database. The main contributions include: (1) we have developed a new max margin learning approach — the enhanced max margin learning framework that is much more efficient in learning with a much faster convergence rate, which is verified in empirical evaluations; (2) we have applied this EMML approach to developing an effective and efficient solution to the multi-modal 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; this advantage is also' supported through the complexity analysis as well as empirical evaluations against a state-of-the-art multimodal data mining method from the literature. While EMML is a general framework, for the evaluation purpose, we apply it to the Berkeley Drosophila embryo image database, and report the performance comparison with a state-of-the-art multimodal data mining method.