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

这一建议是为了开发一个通用的表示框架，使用相似度来捕捉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组合多个核的新方法，学习低阶核，提高标引效率。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。涉及的两个主要研究问题是：(1)定义和计算海量、不断扩大的存储库中图像之间的相似性，并以高效的方式表示这些相似性，以便根据需要检索正确的图像对；以及(2)开发一个系统，可以从稀疏的监督信息和不断变化的数据中学习和预测相似性。这种方法值得注意的是，它接受了网络档案的规模，并使用了语言和视觉分析手段。

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.