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

这个项目研究了一个基本的和关键的，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，试图划分和征服的视频场景，分别分析的视觉对象在很大程度上面临。探索视觉上下文已经显示出其对视频场景理解的承诺。 由于视觉数据中内容的不确定性、视觉环境中结构的不确定性和视觉模式中语义的不确定性，发现视觉环境是一项具有挑战性的任务。该项目的目标是通过追求创新的方法来发现搭配视觉模式，增强视觉模式的上下文匹配，并促进视觉识别的上下文建模，为视频场景理解奠定上下文挖掘和学习的基础。该研究团队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，并提供了促进上下文匹配和建模的方法和工具。这项研究显着推进视频场景建模和理解，并产生了一个重要的使能技术，广泛的应用，包括图像/视频管理和搜索，智能监控和安全，人机交互，社交网络等。这项研究计划有助于通过课程开发，学生参与，研讨会和视觉社区以外的教程教育。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。

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.