Scalable, Multimodal Algorithms for Multimedia Information Retrieval

本研究的目的是提高搜索引擎的能力，以了解用户的查询寻求多媒体数据，加快机器学习算法理解查询，并索引高维图像数据，以允许快速匹配的查询概念找到的数据。这项研究包括三个方面：多模态主动学习，可扩展的内核机器和内核索引。第一个推力探索的方法来配置文件的查询概念的复杂性和方法的概念可以使用图像上下文，图像内容，文本和相机参数的信息来学习。第二个推力研究近似因式分解算法和并行算法，以加快核心机器，如支持向量机（SVM）和核PCA。第三个推力设计索引算法，在潜在的无限维空间中与内核方法一起工作。总之，这三个集成的研究推力提供了一个坚实的基础，建立大规模的，下一代的多媒体信息检索系统。在训练和索引中加速核方法对于使学习在真实的时间和大规模上可行至关重要。这项工作的更广泛的影响预计将是非常重要的，因为各种应用程序依赖于高性能的内核方法来扩展到更大的数据库。这项研究的预期成果包括：一个更快版本的支持向量机，一个内核索引算法，以及一个大规模的图像共享和图像搜索引擎的发展。这些结果将通过开放源码软件或万维网服务，通过项目网址（http：//www.mmdb.ece.ucsb.edu/Wareechang/IIS-0535085.html）传播。

The aim of this research is to advance the ability of a search engine to understand a user's query seeking multimedia data, to speed up machine-learning algorithms for comprehending a query, and to index high-dimensional imagery data to permit fast matching of found data to a query concept. This study comprises three thrusts: multimodal active learning, scalable kernel machines, and kernel indexing. The first thrust explores ways to profile the complexity of a query concept and ways a concept can be learned using information from image context, image content, text, and camera parameters. The second thrust investigates approximate factorization algorithms and parallel algorithms to speed up kernel machines such as Support Vector Machines (SVMs) and kernel PCA. The third thrust devises indexing algorithms to work with the kernel methods in a potentially infinite dimensional space. Together, these three integrated research thrusts provide a solid foundation for building large-scale, next-generation, multimedia information retrieval systems. Speeding up the kernel methods in both training and indexing is critical for making learning feasible in real time and on a large scale. Broader impacts of this work are expected to be very significant because a variety of applications depend on high-performance kernel methods to scale up to larger databases. The expected results of this research include: a faster version of SVMs, a kernel-indexing algorithm, and a large-scale development of an image-sharing and image-search engine. These results will be disseminated via open-source software or World Wide Web services via the project Web site (http://www.mmdb.ece.ucsb.edu/~echang/IIS-0535085.html).