Statistical Learning for Image Annotation

图像标注的统计学习

• 批准号: 1521092
• 负责人: Jia Li
• 金额: $ 32.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521092&HistoricalAwards=false
• 关键词: Statistical; Learning; Image; Annotation

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.

随着计算机网络和移动设备的迅速扩展，技术对管理和充分利用大量图像的需求非常有需求。图像的语义内容分析是这些技术的核心。该项目的目的是开发新的数学工具来解决图像理解中的关键和困惑问题。该项目中要开发的统计学习方法和原型系统将具有广泛的应用，对学术界和行业都有价值。软件包将被分发和维护以供公共访问。该项目将涉及本科生的研究，并使研究生了解广泛的数学主题和跨学科主题。尽管已经利用了先进的统计学习方法来自动注释图像，但现有的方法均均均均均均均均均均均均遭受了图像和限制的数学表征和限制的图像和限制。在这个项目中，将为图像及其语义提出新的统计模型。这些模型将开发出有效的计算估计方法。高级优化技术将用于更好地表征图像。将开发新的大规模聚类算法，用于在Kantorovich-Wasserstein指标下的一组加权和无序的向量。可以预见的是，与图像内容分析相比，该项目中开发的聚类和统计建模方法将具有更广泛的应用程序。