CAREER: Scalable Image Search and Recognition: Learning to Efficiently Leverage Incomplete Information

职业：可扩展图像搜索和识别：学习有效利用不完整信息

• 批准号: 0747356
• 负责人: Kristen Grauman
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747356&HistoricalAwards=false
• 关键词: CAREER; Scalable; Image; Search; Recognition

AbstractTitle: Scalable Image Search and Recognition: Learning to Efficiently Leverage Incomplete InformationPI: Kristen GraumanInstitution: The University of Texas at AustinAs it becomes increasingly feasible to capture, transmit, and store image and video content on a large scale, the need for machine vision algorithms capable of interpreting it is undeniable. The opportunities appear vast, but progress towards large-scale visual recognition hinges on the development of computationally efficient methods that can effectively leverage minimal supervision. The proposed research considers how informative but incomplete cues can contribute to the learning process, with the goal of enabling large volumes of visual data to be efficiently organized and queried, and a greater number of visual categories to be recognized.This project intends to advance the scale of the recognition problem by using fragments of supervision, even when they are inexact or dynamic. The PI and her team will develop methods to allow very large image databases to be searched according to distance functions inferred from sparse similarity constraints. They will consider visual category learning scenarios where the system itself actively requests only the most useful information, and integrates ambiguous cues from external modalities such as text. As knowledge about an image collection evolves over time, so must the associated search structure. The PI will investigate ways to adapt image indexing techniques according to dynamic constraints. The proposed technical plan calls for a combination of ideas from vision, learning, and algorithms. Scalable recognition and image search will affect the extent to which visual data can be accessed and mined, making this work relevant to other scientific disciplines where images capture vital information but currently lack proper tools for large-scale analysis. The project also entails complementary educational and outreach activities aimed at engaging students in research, furthering communication across related areas, and encouraging young students to consider studying computer science or engineering.Updates will be available from: http://www.cs.utexas.edu/¡­grauman/

摘要标题：可扩展的图像搜索和识别：学习有效利用不完整信息PI：Kristen Grauman机构：德克萨斯大学奥斯汀分校随着大规模捕获，传输和存储图像和视频内容变得越来越可行，对机器视觉算法的需求能够解释它是不可否认的。机会似乎很大，但大规模视觉识别的进展取决于计算效率高的方法的发展，这些方法可以有效地利用最小的监督。建议的研究考虑如何信息丰富，但不完整的线索可以有助于学习过程中，使大量的视觉数据被有效地组织和查询的目标，和更多的视觉类别被recognized.This项目的目的是通过使用片段的监督，即使是不准确的或动态的识别问题的规模。PI和她的团队将开发允许根据从稀疏相似性约束推断的距离函数搜索非常大的图像数据库的方法。他们将考虑视觉类别学习场景，其中系统本身只主动请求最有用的信息，并整合来自外部模态（如文本）的模糊线索。随着关于图像集合的知识随着时间的推移而发展，相关联的搜索结构也必须如此。PI将研究如何根据动态约束调整图像索引技术。拟议的技术计划需要结合视觉，学习和算法的想法。可扩展的识别和图像搜索将影响视觉数据的访问和挖掘程度，使这项工作与其他科学学科相关，其中图像捕获重要信息，但目前缺乏适当的大规模分析工具。该项目还需要补充教育和推广活动，旨在吸引学生参与研究，促进相关领域的交流，并鼓励年轻学生考虑学习计算机科学或工程。