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

这个建议是开发一个通用的表示框架，使用相似性来捕获大规模图像集合中的关系。这种表示不局限于任何特定的距离函数、特征或学习模型。它包含了基于不同线索组合多个核、学习低秩核和提高索引效率的新方法。此外，还提出了最近邻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。两个主要的研究问题是：(1)定义和计算庞大的、不断扩展的存储库中图像之间的相似性，并以有效的方式表示这些相似性，以便可以根据需要检索到正确的对；(2)开发一个系统，可以通过“稀疏的监督信息和不断发展的数据”来学习和预测相似性。这种方法的显著之处在于它对网络档案规模的拥抱，以及它对口头和视觉分析手段的使用。

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.