Collaborative Research: Content Based Queries in Image Databases

这是一个跨机构合作项目，加州大学圣地亚哥分校的拉梅什·贾恩和威斯康星大学麦迪逊分校的拉古·拉马克里希南参与了这个项目。该研究集中于对存储在通用(即独立于应用领域)数据库中的大量图像集合进行基于内容的查询。目标是开发用于通过利用图像组(例如，一组正面面部图像)中的相似性来处理非常大量的图像的可伸缩方法。所采取的方法是向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类别。该系统结构被设计成可扩展的，因为可以容易地结合用户提供的特征提取算法，并且利用了高效的高维索引技术。虽然已经有许多努力来构建能够有效地从给定图像域检索图像的系统，但通用系统提出了许多困难的问题。这项研究有望极大地提高基于内容的查询的表达能力，使其在通用图像数据库中得到有效支持，从而使涉及来自不同领域的大量图像的新型应用成为可能。

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.