基于内容的图象检索技术  

A Survey on Content-based Image Retrieval Techniques

作　　者：徐杰[1] 施鹏飞[1] 

机构地区：[1]上海交通大学模式识别与图象处理研究所,上海200030

出　　处：《中国图象图形学报（A辑）》2003年第9期977-983,共7页Journal of Image and Graphics

基　　金：上海市科委发展基金项目(015115022)

摘　　要：随着数字图象的日益增多,基于内容的图象检索已成为图象使用者和管理者迫切需要解决的问题,近年来,各国研究者纷纷加入该领域的研究.为了使人们对该领域现状有个概略了解,以推动该领域研究进一步开展,首先概括介绍了基于内容图象检索的产生、发展及其关键技术;然后介绍了特征提取(包括低层特征和语义特征)及其相似性计算、相关反馈等的原理及算法;最后指出了基于内容的图象检索技术与计算机视觉技术的区别所在,并对目前存在的问题和应着重的研究内容以及发展方向进行了分析.Owing to the widespread use of digital images, methods for efficient image access, management and retrieval become urgent requirements to image users and managers. Content-based image retrieval (CBIR) is a good solution for this problem, and has attracted increasing attention from researchers all over the world recently. In this paper, we review the beginning and various applications of CBIR firstly, and then we introduce some key techniques and algorithms for CBIR, such as methods and principals for image feature selection and representation, feature-based similarity computation, semantic features and relevance feedback. CBIR inherits some automatic techniques from traditional computer vision. However, CBIR and computer vision are very different in essence. We put forward our views on both common and distinct characters between them in the concluding section. CBIR is distinguished by the ability of on-line learning through interactive with users. Future research directions are also be presented in this section, including relevance feedback, features fusion, database technique and hierarchical ordered descriptions about the semantic content of images.

关 键 词：图象检索技术 数字图象 特征提取 相似性 目标识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP391.3[自动化与计算机技术—计算机科学与技术]