CAREER: Generalized Image Understanding with Probabilistic Ontologies and Dynamic Adaptive Graph Hierarchies

职业：利用概率本体论和动态自适应图层次结构进行广义图像理解

• 批准号: 0845282
• 负责人: Venkat Krovi
• 金额: $ 53.91万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845282&HistoricalAwards=false
• 关键词: CAREER; Generalized; Image; Understanding; Probabilistic

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).From representation to learning to inference, effective use of high-level semantic knowledge in computer vision remains a challenge in bridging the signal-symbol gap. This research investigates the role of semantics in visual inference through the generalized image understanding problem: to automatically detect, localize, segment, and recognize the core high-level elements and how they interact in an image, and provide a parsimonious semantic description of the image.Specifically, this research examines a unified methodology that integrates low- (e.g., pixels and features), mid- (e.g. latent structure), and high-level (e.g., semantics) elements for visual inference. Adaptive graph hierarchies induced directly from the images provide the core mathematical representation. A statistical interpretation of affinities between neighboring pixels and regions in the image drives this induction. Latent elements and structure are captured with multilevel Markov networks. A probabilistic ontology represents the core knowledge and uncertainty of the inferred structure and guides the ultimate semantic interpretation of the image. At each level, rigorous methods from computer science and statistics are connected to and combined with formal semantic methods from philosophy.A symbiotic education plan involving graduate and undergraduate mentoring and education, professional tutorial courses at the boundary of vision and ontology, and K-12 outreach is incorporated into the research plan. The research and education, disseminated broadly through both the applied science and semantics/philosophy literatures, lays a foundation on which to both utilize and automatically extract rich semantic information from images and other signal data for critical application areas such as internet vision, autonomous navigation, and ambient biometrics.

该奖项由2009年美国复苏和再投资法案（公法111-5）资助。从表示到学习再到推理，在计算机视觉中有效使用高级语义知识仍然是弥合信号-符号差距的挑战。 本研究通过广义的图像理解问题来研究语义在视觉推理中的作用：自动检测、定位、分割和识别图像中的核心高级元素以及它们如何相互作用，并提供图像的简约语义描述。像素和特征）、中间（例如潜在结构）和高级（例如，语义）元素进行视觉推理。 直接从图像中导出的自适应图层次结构提供了核心的数学表示。 图像中相邻像素和区域之间的亲和力的统计解释驱动了这种归纳。 潜在元素和结构的捕获与多级马尔可夫网络。 概率本体表示推断结构的核心知识和不确定性，并指导图像的最终语义解释。 在每一个层次上，严格的方法，从计算机科学和统计学连接，并结合正式的语义方法从philosophy.A共生教育计划，包括研究生和本科生的指导和教育，专业辅导课程的边界的视觉和本体论，和K-12推广纳入研究计划。 通过应用科学和语义学/哲学文献广泛传播的研究和教育为利用和自动从图像和其他信号数据中提取丰富的语义信息奠定了基础，这些信息用于关键应用领域，如互联网视觉，自主导航和环境生物识别。