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基于内容的图像检索技术
引用本文:张涛,张星明.基于内容的图像检索技术[J].广州大学学报(自然科学版),2004,3(5):432-437,465.
作者姓名:张涛  张星明
作者单位:1. 华南师范大学,广东,广州,510631
2. 华南理工大学,计算机与工程学院,广东,广州,510641
摘    要:综述了CBIR的需要包括适应大量图像数据库的存储管理和查询应用;介绍了CBIR的系统结构.CBIR的关键技术是基于内容的图像数据库结构,象素域的图像查询检索和压缩域的图像查询检索;CBIR现有的系统有QBIC,Virage,VisualSEEK,PhotoBook和MARS.还对CBIR存在的问题和研究方向进行了分析.CBIR的主要研究方向为评价技术、基于语义内容的图像检索、数据模型、综合检索手段和多维索引技术。

关 键 词:基于内容的图像检索  基于内容的图像数据库  象索域的图像  压缩域的图像
文章编号:1671-4229(2004)05-0432-06

Techniques of content-based image retrieval
ZHANG Tao,ZHANG Xing-ming.Techniques of content-based image retrieval[J].Journal og Guangzhou University:Natural Science Edition,2004,3(5):432-437,465.
Authors:ZHANG Tao  ZHANG Xing-ming
Institution:ZHANG Tao~1,ZHANG Xing-ming~2
Abstract:Owing to the widespread use of digital images, methods for efficient image access, image management and image retrieval have become urgent requirements to image users. Content-based image retrieval (CBIR) has attracted increasing attention from researchers for it is an excellent solution to the problem. In this paper, we reviewed the requirements of CBIR technique including adaptation to the storage management for large database of images and query application. We introduced the system structures for CBIR. The key techniques for CBIR are the structure of content-based image database, the query retrieval of pixel field images and the query retrieval of compress field images. The existing systems for CBIR include QBIC, Virage, Visual SEEK, PhotoBook and MARS. We also analyzed the existing problems and the research trends for CBIR. The major research trends for CBIR are evaluation technology, semantic content-based image retrieval, data model, integrated measurement for retrieval and multi-dimensional indexing technology.
Keywords:content-based image retrieval  content-based image database  pixel field images  compress field images
本文献已被 CNKI 维普 万方数据 等数据库收录!
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