RI: Small: Mining and Learning Visual Contexts for Video Scene Understanding

RI：小：挖掘和学习视频场景理解的视觉上下文

• 批准号: 1217302
• 负责人: Ying Wu
• 金额: $ 42.89万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217302&HistoricalAwards=false
• 关键词: RI; Small; Mining; Learning; Visual

This project investigates a fundamental and critical, but largely unexplored issue: automatically identifying visual contexts and discovering visual patterns. Many contemporary approaches that attempt to divide and conquer the video scenes by analyzing the visual objects separately are largely confronted. Exploring visual context has shown its promise for video scene understanding. Discovering visual contexts is a challenging task, due to the content uncertainty in visual data, structure uncertainty in visual contexts, and semantic uncertainty in visual patterns. The goal of this project is to lay the foundation of contextual mining and learning for video scene understanding, by pursuing innovative approaches to discovering collocation visual patterns, to empowering contextual matching of visual patterns, and to facilitating contextual modeling for visual recognition. The research team develops a unified approach to mining visual collocation patterns and learning visual contexts, and to provide methods and tools that facilitate contextual matching and modeling. This research significantly advances video scene modeling and understanding, and produces an important enabling technology for a wide range of applications including image/video management and search, intelligent surveillance and security, human-computer interaction, social networks, etc. This research program contributes to education through curriculum development, student involvements, and workshops and tutorials outside the vision community. This project also outreaches to K-12 education, and it provides datasets and software on its website to the community.

这个项目研究了一个基本的、关键的、但在很大程度上未被探索的问题：自动识别视觉上下文和发现视觉模式。许多当代的方法试图通过单独分析视觉对象来分割和征服视频场景，这在很大程度上面临着挑战。探索视觉情境已经显示出它在视频场景理解方面的前景。由于视觉数据中的内容不确定性、视觉语境中的结构不确定性以及视觉模式中的语义不确定性，视觉语境的发现是一项具有挑战性的任务。这个项目的目标是通过寻求发现搭配视觉模式的创新方法，增强视觉模式的语境匹配，并促进用于视觉识别的语境建模，为视频场景理解奠定语境挖掘和学习的基础。研究小组开发了一种统一的方法来挖掘视觉搭配模式和学习视觉上下文，并提供促进上下文匹配和建模的方法和工具。这项研究极大地促进了视频场景建模和理解，并为图像/视频管理和搜索、智能监控和安全、人机交互、社交网络等广泛应用提供了重要的使能技术。该研究计划通过课程开发、学生参与以及VISION社区以外的研讨会和教程来促进教育。该项目还延伸到K-12教育，并在其网站上向社区提供数据集和软件。