多特征融合的图像格贴近度匹配方法

肖满生; 肖哲; 万烂军

doi:10.7652/xjtuxb201904017

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多特征融合的图像格贴近度匹配方法

更新时间：2025-07-09

- 多特征融合的图像格贴近度匹配方法
- A Method of Image Content Matching with Lattice Closeness and Multi-Feature Fusion
- 西安交通大学学报 2019年53卷第4期页码：115-121
- 作者机构：
  
  湖南工业大学计算机学院,湖南,株洲,412007
- 作者简介：
- 基金信息：
  
  : 2018-09-20。作者简介: 肖满生(1968—),男,教授,硕士生导师。基金项目: 国家自然科学青年科学基金资助项目(61702177);湖南省自然科学基金资助项目(2018JJ4068);湖南省高铁运行安全保障工程技术研究中心开放基金资助项目(2017TP2022-17KJ104)
- DOI：10.7652/xjtuxb201904017
  中图分类号： TP391
- 网络首发：2019-04-10，
  
  纸质出版：2019
- 稿件说明：
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肖满生, 肖哲, 万烂军. 多特征融合的图像格贴近度匹配方法[J]. 西安交通大学学报, 2019,53(4):115-121.

A Method of Image Content Matching with Lattice Closeness and Multi-Feature Fusion[J]. 2019, 53(4): 115-121.
肖满生, 肖哲, 万烂军. 多特征融合的图像格贴近度匹配方法[J]. 西安交通大学学报, 2019,53(4):115-121. DOI： 10.7652/xjtuxb201904017.

A Method of Image Content Matching with Lattice Closeness and Multi-Feature Fusion[J]. 2019, 53(4): 115-121. DOI： 10.7652/xjtuxb201904017.

摘要

针对目前基于内容的图像检索方法中存在噪声、边界划分模糊及相似匹配算法复杂而检索精度不高的问题

提出了一种多特征融合的格贴近度图像内容匹配方法。为了抑制噪声

对像素间的灰度关系S

与空间关系S

进行合成

提出了邻域窗口像素灰度特征描述子; 为了对组成图像的对象的边界进行分割

设计了一个像素密度分布特征描述子; 在对图像的灰度、纹理及密度特征进行融合的基础上

提出了一种格贴近度图像匹配方法

实现了两图像相似性比较。实验结果表明:在灰度图像的相似性检索中

多特征融合的格贴近度匹配方法与特征压缩匹配及兴趣区域匹配等其他方法相比

平均归一化修正检索秩低10%左右

查准率-查全率综合评价指标高5%左右

该方法不仅设计简单

而且具有较高的检索精度。

Abstract

An image content matching method with lattice closeness and multi-feature fusion is proposed to improve the problem of low retrieval performance in content-based image retrieval. Firstly

an original image is scaled and segmented by clustering to obtain various objects of the image. Then

a gray feature descriptor is defined taking pixel gray and spatial relations into account. The texture and pixel density distribution of the object are integrated

and a set of feature vectors is obtained according to their weights. Lastly

the matching degree of the image is calculated based on lattice closeness. Theoretical analysis and experimental results show that this method has higher retrieval precision in the similarity matching retrieval of gray images.

关键词

Keywords

references

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