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

摘要标题：可伸缩图像搜索和识别：学习有效地利用不完整信息PI：Kristen GraumanInstitution：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，对能够解释它的机器视觉算法的需求是不可否认的。机会似乎是巨大的，但大规模视觉识别的进展取决于计算效率高的方法的开发，这些方法可以有效地利用最少的监督。这项研究考虑了信息丰富但不完整的线索如何有助于学习过程，目的是使大量的视觉数据能够被有效地组织和查询，以及更多的视觉类别被识别。该项目旨在通过使用监督片段来提高识别问题的规模，即使它们是不准确的或动态的。PI和她的团队将开发方法，允许根据稀疏相似性约束推断的距离函数来搜索非常大的图像数据库。他们将考虑视觉类别学习情景，其中系统本身只主动请求最有用的信息，并整合来自文本等外部模式的模棱两可的线索。随着关于图像集合的知识随着时间的推移而发展，相关的搜索结构也必须发展。PI将研究根据动态约束调整图像索引技术的方法。拟议的技术计划要求将愿景、学习和算法的想法结合在一起。可伸缩识别和图像搜索将影响可访问和挖掘可视数据的程度，使这项工作与图像捕捉重要信息但目前缺乏适当工具进行大规模分析的其他科学学科相关。该项目还包括互补性的教育和推广活动，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学。最新信息可从以下网站获得：http://www.cs.utexas.edu/...

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/