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。该表示不限于任何特定的距离函数、特征或学习模型。它包括基于不同线索的联合收割机多个内核的新方法，学习低秩内核，并提高索引效率。此外，提出了新的最近邻搜索和半监督学习方法。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）定义和计算图像之间的相似性，在巨大的，不断扩大的，存储库，并表示这些相似性在一个有效的方式，使正确的对可以检索的需求;（2）开发一个系统，可以学习和预测相似性与稀疏的监督信息和不断发展的数据。“这种方法值得注意的是，它拥抱了网络档案的规模，并使用了口头和视觉分析手段。

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.