Scalable, Multimodal Algorithms for Multimedia Information Retrieval

这项研究的目的是提高搜索引擎理解用户寻找多媒体数据的查询的能力，加快理解查询的机器学习算法，并索引高维图像数据以允许发现的数据与查询概念的快速匹配。该研究包括三个方面的内容：多通道主动学习、可扩展核机器和核索引。第一个重点是探索描述查询概念的复杂性的方法，以及使用来自图像上下文、图像内容、文本和相机参数的信息学习概念的方法。第二个推力研究了近似因式分解算法和加速核机器的并行算法，如支持向量机和核主成分分析。第三个推力设计了索引算法，以便在潜在的无限维空间中与核方法一起工作。这三项综合研究为构建大规模、下一代、多媒体信息检索系统提供了坚实的基础。加快训练和索引中的核心方法是使学习实时和大规模可行的关键。这项工作的更广泛影响预计将非常显著，因为各种应用程序依赖于高性能内核方法来向上扩展到更大的数据库。这项研究的预期结果包括：一个更快的支持向量机版本，一个内核索引算法，以及一个图像共享和图像搜索引擎的大规模开发。这些成果将通过开源软件或万维网服务通过项目网站(http://www.mmdb.ece.ucsb.edu/~echang/IIS-0535085.html).发布

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).