Statistical Learning for Image Annotation

随着计算机网络和移动设备的迅速扩展，对管理和充分利用大量图像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。本项目所开发的统计学习方法和原型系统将具有广泛的应用价值，对学术界和工业界都有价值。将分发和维护软件包，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主题。虽然先进的统计学习方法已经被用于自动注释图像，但现有的方法在图像的数学表征和注释词和图像组件的建模方面都存在缺陷。在这个项目中，将为图像及其语义提出新的统计模型。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在Kantorovich-Wasserstein度量下为加权和无序向量集开发。预计本项目开发的聚类和统计建模方法将比图像内容分析具有更广泛的应用。

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.