摘要
在基于内容的图像检索中,不同图像特征反映了图像不同侧面的内在特性,如何有效地组织和利用这些特征从而提高系统的检索性能是一个值得研究的课题.首先提出了特征互补率的定义,通过计算互补矩阵有指导地选择融合特征集.实验结果表明,互补矩阵能够很好地估计特征之间的补充能力.同时提出了基于平均检索精度的特征线性融合方法,并在一个包含12000张异质图像的大型图像库上与当前图像检索中最常用的几种方法进行了对比实验,结果表明这种方法具有更高的精度.
In content-based image retrieval, image has various inherent aspects which reflect its contents, therefore how to organize and utilize these contents effectively to improve the retrieval performance is a valuable research topic. In this paper, a method of measuring the complementarities between two feature spaces is proposed, based on which the fusion feature set can be selected effectually, and the experimental results are positive. At the same time, a linear fusion method based on the average precision of features is proposed. Extensive comparisons against several methods, such as flat model and rank-based linear fusion are performed. Experiments are carried out on a large-size heterogeneous image collection consisting of 12000 images and the results demonstrate the effectiveness of the proposed method.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2005年第9期1640-1646,共7页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展规划基金项目(2004CB318108)
国家自然科学基金项目(60223004
60321002
60303005)
教育部科学技术研究重点基金项目(104236)~~
关键词
基于内容的图像检索
特征融合
平均检索精度
content-based image retrieval（CBIR）
feature fusion
average retrieval precision
