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

SGER：大型图像数据库中基于相关性反馈内容的检索的灵活索引结构

• 批准号: 0136348
• 负责人: Jing Peng
• 金额: $ 5万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-11-01 至 2002-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0136348&HistoricalAwards=false
• 关键词: SGER; Flexible; Index; Structure; Relevance

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.

本计画的目标是设计有效的索引策略，借由相关回馈学习来支援弹性的检索尺度。然而，试图同时满足这两个目标（效率和灵活性）会导致冲突。探索一种新的方法来捕捉灵活的指标和索引，有可能解决冲突之间的内在相互作用。据推测，可以利用这种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。这个探索性的项目旨在建立一种方法的概念证明，这种方法可以权衡准确性和效率，并且可以避免在大规模图像数据库中进行穷举搜索。待探索的方法是基于在高维数据中的颠簸狩猎，用于诱导一组（可能重叠的）捕获本地数据分布的框。诱导框有效地覆盖特征空间，从而提供对图像数据库的索引。新的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，支持各种查询类型，从查询的图像查询的区域。如果成功，该项目的结果将使灵活的度量学习在大规模图像数据库中的使用成为可能，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。