CAREER: Similarity-based Representation of Large-scale Image Collections

职业：大规模图像集合的基于相似性的表示

• 批准号: 1228082
• 负责人: Svetlana Lazebnik
• 金额: $ 37.21万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228082&HistoricalAwards=false
• 关键词: CAREER; Similarity; based; Representation; Large

This proposal is to develop a general representation framework that uses similarity to capture relationships in large scale image collections. The representation is not restricted to any specific distance function, feature, or learning model. It includes new methods to combine multiple kernels based on different cues, learn low-rank kernels, and improve indexing efficiency. In addition, new methods for nearest neighbor search and semi-supervised learning are proposed. It has relevance to machine learning and computer vision research agendas. Two major research problems addressed are: (1) defining and computing similarities between images' in vast, expanding, repositories, and representing those similarities in an efficient manner so the right pairs can be retrieved on demand; and (2) developing a system that can learn and predict similarities with 'sparse supervisory information and constantly evolving data.' The approach is notable in its embrace of the scale of web archives and its use of verbal and visual means of analysis.

该建议是开发一个通用表示框架，该框架使用相似性来捕获大规模图像集中的关系。表示形式不仅限于任何特定的距离功能，功能或学习模型。它包括新的方法，可以根据不同的提示组合多个内核，学习低级内核并提高索引效率。此外，提出了最近的邻居搜索和半监督学习的新方法。它与机器学习和计算机视觉研究议程相关。 解决的两个主要研究问题是：（1）定义和计算图像在广阔，扩展，存储库中的相似性，并以有效的方式表示这些相似之处，以便可以根据需要检索正确的对； （2）开发一个可以学习和预测“稀疏监督信息并不断发展数据”的系统。这种方法在对网络档案的规模及其对口头和视觉分析的使用方面的拥抱中很有意义。