Statistical Learning for Image Annotation

随着计算机网络和移动的设备的快速扩展，对于管理和最佳利用大量图像集合的技术有很大的需求。图像的语义内容分析是这些技术的核心。该项目的目标是开发新的数学工具来解决图像理解中的关键和复杂问题。本计画所发展之统计学习方法与原型系统，将具有广泛之应用价值，对于学术界与工业界皆有价值。软件包将分发和维护，供公众使用。该项目将涉及本科生的研究，并使研究生接触到广泛的数学主题和跨学科的topics.Although先进的统计学习方法已被利用来自动注释图像，现有的方法遭受的缺陷，在图像的数学表征和注释单词和图像组件的建模的局限性。在这个项目中，新的统计模型将被提出的图像及其语义。将为这些模型开发计算效率高的估计方法。先进的优化技术将用于更好地表征图像。新的大规模聚类算法将开发的加权和无序向量集下的Kantorovich-Wasserstein度量。可以预见，在这个项目中开发的聚类和统计建模方法将有更广泛的应用比图像内容分析。

With the rapid expansion of computer networks and mobile devices, technologies are in great demand for managing and making best use of large collections of images. Semantic content analysis of images is at the heart of these technologies. The objective of this project is to develop new mathematical tools to tackle crucial and perplexing problems in image understanding. The statistical learning methods and the prototypical system to be developed in this project will have wide applications, valuable for both academia and industry. Software packages will be distributed and maintained for public access. This project will involve undergraduate students in research, and expose graduate students to a broad range of mathematical topics and interdisciplinary topics.Although advanced statistical learning methods have been exploited to annotate images automatically, existing approaches suffer from both flaws in the mathematical characterization of images and limitations in the modeling of annotation words and image components. In this project, new statistical models will be proposed for images and their semantics. Computationally efficient estimation methods will be developed for these models. Advanced optimization techniques will be used to better characterize images. New large-scale clustering algorithms will be developed for sets of weighted and unordered vectors under the Kantorovich-Wasserstein metric. It is envisioned that the clustering and statistical modeling methods developed in this project will have broader applications than image content analysis.