Personalized image searching based on clustering analysis and user interest model

GUO Lu; SONG Jing; DU Jing-jing; XU Zhe; HUANG He

doi:10.3788/OPE.20172513.0199

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Personalized image searching based on clustering analysis and user interest model

更新时间：2020-08-13

- Personalized image searching based on clustering analysis and user interest model
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 199-204(2017)
- 作者机构：
  
  1. 中电科第二十研究所, 陕西西安 710068
  2. 西安爱生技术集团公司, 陕西西安 710065
  3. 无人机系统国家工程研究中心,陕西西安,710072
  4. 长安大学电子与控制工程学院,陕西西安,710064
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0199
  CLC： TP391.4
- Received：24 May 2017，
  
  Revised：19 June 2017，
  
  Published：25 November 2017
- 稿件说明：
移动端阅览
郭璐, 宋京, 杜晶晶等. 基于聚类和用户兴趣模型的个性化图像搜索[J]. 光学精密工程, 2017,25(10s): 199-204

GUO Lu, SONG Jing, DU Jing-jing etc. Personalized image searching based on clustering analysis and user interest model[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 199-204
郭璐, 宋京, 杜晶晶等. 基于聚类和用户兴趣模型的个性化图像搜索[J]. 光学精密工程, 2017,25(10s): 199-204 DOI： 10.3788/OPE.20172513.0199.

GUO Lu, SONG Jing, DU Jing-jing etc. Personalized image searching based on clustering analysis and user interest model[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 199-204 DOI： 10.3788/OPE.20172513.0199.

摘要

针对现有图像搜索方法难以完整地评价用户查询目的及图像检索质量较低的问题，提出了一种基于聚类和用户兴趣模型的个性化图像搜索方法。该方法首先将输入的检索图像拆分成9个子块，并将图像转换到HSV颜色空间中，利用图像的颜色分布直方图提取颜色特征信息；然后采用Gabor小波提取图像的纹理特征，将获取到的颜色特征和纹理特征进行融合以构成图像的多特征融合相似度矩阵，计算图像间的相似性。再以多特征融合相似度矩阵为多核的动态聚类的输入，对数据库中图像进行聚类，将聚类图像送入LSSVM网络，确定分类面并构建个性化用户兴趣模型；最后按照与用户兴趣模型相似度高低程度进行比较，将检索结果提供给用户自主选择。实验结果表明：与单一的颜色和纹理特征搜索算法相比，所提方法的平均查全率、查准率分别提升了8.2%、11.42%和19.7%、26.08%，可以有效地提升图像搜索的质量，应用价值明显。

Abstract

Aimed at the problem that existing image searching algorithms were difficult to evaluate query purpose of users completely and the quality of image retrieval was low

a personalized image searching method based on clustering analysis and user interest model was proposed in the thesis. Input retrieval image was divided into 9 sub-blocks and transformed into HSV color space

and color distribution histogram of image was used to extract color feature information. And then

Gabor wavelet was used to extract texture features of the image

and obtained color features and texture features were fused to form multi-feature fusion similarity matrix of the image to calculate similarity among images. Then multi-feature fusion similarity matrix was used as input of multi-core dynamic clustering to cluster the images in the database. The clustering image was sent to LSSVM network to determine the classification surface and construct personalized user interest model. Finally

retrieved results were provided to users for independent choice according to comparison with similarity degree of user interest model. Experimental result shows that:average recall ratio and precision ratio of method in the thesis are improved by 8.2%

11.42% and 19.7%

26.08% compared with search algorithm of single color and texture features. It can promote quality of image searching effectively and has obvious application value.

关键词

Keywords

references

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