Research on Image Retrieval Based on Fusion the Scale Free Particle Swarm Optimization

JIN Yan-xia; XUE Dan; ZHANG Xin; LI Wei-nan

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Research on Image Retrieval Based on Fusion the Scale Free Particle Swarm Optimization

更新时间：2026-09-04

- Research on Image Retrieval Based on Fusion the Scale Free Particle Swarm Optimization
- Vol. 35, Issue 1, Pages: 36-40(2018)
- 作者机构：
  
  1. 中北大学机构计算机与控制工程学院,山西,太原,030051
  2. 太原市气象局,山西,太原,030051
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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靳雁霞, 薛丹, 张鑫, 李伟楠. 融合类无标度粒子群算法的图像检索研究[J]. 微电子学与计算机, 2018, 35(1): 36-40.

JIN Yan-xia, XUE Dan, ZHANG Xin, et al. Research on Image Retrieval Based on Fusion the Scale Free Particle Swarm Optimization[J]. 2018, 35(1): 36-40.
靳雁霞, 薛丹, 张鑫, 李伟楠. 融合类无标度粒子群算法的图像检索研究[J]. 微电子学与计算机, 2018, 35(1): 36-40. DOI：

JIN Yan-xia, XUE Dan, ZHANG Xin, et al. Research on Image Retrieval Based on Fusion the Scale Free Particle Swarm Optimization[J]. 2018, 35(1): 36-40. DOI：

摘要

提出一种融合类无标度网的禁忌粒子群(ST-PSO)算法

并用其优化改进后的图像检索相关反馈过程.该方法通过融合类无标度网的禁忌粒子群算法优化反馈过程

并根据用户的反馈信息引入分辨率来动态调整特征权重以及查询向量.通过实验验证了该方法能够增加粒子种群多样性

避免早熟收敛

增强粒子跳出局部最优解的能力

并且可以检索到更加符合用户需求的图片

有效地提高了检索效率和检索精度.

Abstract

This paper propose a Tabu search particle swarm algorithm which fusions the Scale Free Like network to optimize the improved image retrieval relevance feedback.This method opitimizes the feedback process by the Tabu search particle swarm algorithm which fusions the Scale Free Like network and lead into resolution ratio through users feedback information to adjust the feature weight and the query vector dynamically.Experiment shows that the method can not only increase the diversity of particle population

avoid premature convergence

enhance the ability of particle to jump out of local optimal solution

but also retrieve pictures that more user-friendly

effectively improved the retrieval efficiency and retrieval accuracy.

关键词

Keywords

references

Xu X, Liu X, Yu Z, et al. Re-weighting relevance feedback image retrieval algorithm based on particle swarm optimization[C]// Sixth International Conference on Natural Computation. IEEE, Valencia, Spain, 2010: 3609-3613.

Broilo M, Rocca P, De Natale F G B. Content-based image retrieval by a semi-supervised particle swarm optimization[C]// Multimedia Signal Processing, 2008 IEEE, Workshop on. IEEE Xplore, Cairns, Queensland, Australia, 2008: 666-671.

Broilo M, De Natale F G B. A stochastic approach to image retrieval using relevance feedback and particle swarm optimization[J]. IEEE Transactions on Multimedia, 2010, 12(4): 267-277. DOI: 267

余武. 基于内容的图像检索及相关反馈技术研究[D]. 吉林, 长春: 东北师范大学, 2014.

Su J H, Huang W J, Yu P S, et al. Efficient relevance feedback for content-based image retrieval by mining user navigation patterns[J]. IEEE Transactions on Knowledge % Data Engineering, 2011, 23(3): 360-372. DOI: 360

孙艳红. 基于感兴趣区域的融合多特征图像检索方法研究[D]. 重庆: 重庆交通大学, 2010.

符保龙, 张爱科. 基于改进布谷鸟搜索算法的相关反馈图像检索[J]. 电视技术, 2014, 38(3): 39-42. DOI: 39

李勇刚, 邓艳青. 结合禁忌搜索的改进粒子群优化算法[J]. 计算机工程, 2012, 38(18): 155-157. DOI: 155

傅启明, 刘全, 王晓燕, 等. 遗传反馈的多特征图像检索[J]. 中国图象图形学报, 2011, 16(10): 1858-1865. DOI: 1858

李庆先. 基于内容的图像检索相关反馈算法的改进[J]. 计算技术与自动化, 2007, 26(2): 38-41. DOI: 38

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