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

题目：可扩展图像搜索和识别：学习有效地利用不完整信息pi: Kristen graumann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，对能够解释这些内容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的方法的发展，这些方法可以有效地利用最小的监督。该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，目标是使大量视觉数据能够有效地组织和查询，并识别更多的视觉类别。这个项目打算通过使用监督片段来推进识别问题的规模，即使它们是不精确的或动态的。PI和她的团队将开发方法，允许根据从稀疏相似约束推断的距离函数搜索非常大的图像数据库。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，并集成来自外部模式（如文本）的模糊线索。随着时间的推移，关于图像集合的知识不断发展，相关的搜索结构也必须发展。PI将根据动态约束研究适应图像索引技术的方法。提出的技术计划需要将视觉、学习和算法的想法结合起来。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。该项目还包括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。更新将从http://www.cs.utexas.edu/ -grauman /获得

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/