SGER: Flexible Index Structure for Relevance Feedback Content-Based Retrieval in Large Image Databases

该项目的目标是设计有效的索引策略，通过相关反馈学习支持灵活的检索度量。然而，试图同时满足这两个目标（效率和灵活性）会导致冲突。探索了一种新的方法来捕捉灵活度量和索引之间的内在相互作用，这有可能解决冲突。假设可以利用相互作用来创建有效的基于内容的检索系统，以满足实际图像数据库应用中遇到的性能和计算挑战。这个探索性项目旨在建立一种方法的概念证明，这种方法可以用准确性换取效率，并且可以避免在大规模图像数据库中进行穷举搜索。要探索的方法是基于高维数据中的碰撞搜索，以诱导一组（可能重叠的）框来捕获局部数据分布。诱导盒有效地覆盖了特征空间，从而为图像数据库提供了索引。这种新索引技术的灵活性和效率将在异构图像数据库中进行测试，这些数据库支持从按图像查询到按区域查询的各种查询类型。如果成功，该项目的成果将使在大型图像数据库中使用灵活的度量学习成为可能，这将对医疗保健、科学图像、教育或艺术等广泛领域的基于内容的图像检索产生重大影响。

The objective of this project is to design efficient indexing strategies that support flexible retrieval metric through relevance feedback learning. However, trying to satisfy both goals (efficiency and flexibility) at the same time leads to a conflict. A novel approach is explored to capture the inherent interplay between flexible metrics and indexing that has the potential to resolve the conflict. It is hypothesized that the interplay can be exploited to create effective content-based retrieval systems that meet performance and computational challenges encountered in practical image database applications. This exploratory project seeks to establish the proof of concept of an approach that trades off accuracy for efficiency, and that can avoid exhaustive search in large-scale image databases. The methods to be explored are based on bump-hunting in high-dimensional data for inducing a set of (possibly overlapping) boxes that capture the local data distributions. The induced boxes effectively cover the feature space, thereby providing an index to the image database. The flexibility and efficiency of the novel indexing technique will be tested in heterogeneous image databases that support a variety of query types, ranging from query-by-image to query-by-region. If successful, the results of this project will enable the use of flexible metric learning in large scale image databases, which will have a significant impact in content-based image retrieval in broad areas such as health-care, scientific images, education, or art.