Collaborative Research: Content Based Queries in Image Databases

这是一个机构间的合作项目，参与者包括圣地亚哥加州大学的Ramesh Jain和麦迪逊威斯康星州大学的Raghu Ramakrishnan。该研究集中于对存储在通用（即，独立于应用程序域）数据库。目标是 开发用于通过利用图像组中的相似性来处理非常大量的图像的可缩放方法，例如，一组正面人脸图像。所采取的方法是为用户提供足够详细地描述图像集合的能力，以允许系统定制特征提取、索引等，以支持丰富的基于内容的查询。描述是在一个“图像数据定义语言”，并指定在图像特征，如颜色，形状等系统架构设计为可扩展的，用户提供的特征提取算法可以很容易地纳入，并有效的高维索引 技术得到利用。虽然已经有许多努力来构建可以从给定图像域有效地检索图像的系统，但是通用系统提出了许多困难的问题。这项研究预计将大大增加基于内容的查询，可以有效地支持在一般的图像数据库的表现力，使可能涉及大量的图像来自不同领域的新的应用程序。

This is an inter-institutional collaborative project involving Ramesh Jain at the University of California, San Diego and Raghu Ramakrishnan at the University of Wisconsin, Madison. The research focuses on content-based querying of large collections of images stored in a general-purpose (i.e., application domain independent) database. The goal is to develop scalable methods for dealing with very large numbers of images by exploiting similarities in groups of images, e.g., a group of frontal face images. The approach taken is to provide the user with the ability to describe a collection of images in sufficient detail to allow the system to tailor feature extraction, indexing, etc., to suppo rt a rich class of content-based queries. Descriptions are in an "image data definition language" and are specified in terms of image characteristics such as color, shape, etc. The system architecture is designed to be extensible in that user-provided feature extraction algorithms can be easily incorporated, and efficient high-dimensional indexing techniques are utilized. While there have been many efforts to build systems that can effectively retrieve images from a given image domain, a general purpose system poses many difficult problems. This research is expected to greatly increase the expressiveness of content-based queries that can be efficiently supported in general image databases, making possible novel applications involving large numbers of images from diverse domains.