Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm

BI Xiao-jun; PAN Tie-wen

doi:10.3969/j.issn.0372-2112.2017.07.017

您当前的位置：

首页 >

文章列表页 >

Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm

更新时间：2025-07-16

- Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm
- Acta Electronica Sinica Vol. 45, Issue 7, Pages: 1668-1676(2017)
- 作者机构：
  
  哈尔滨工程大学信息与通信工程学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61175126);National International Science and Technology Cooperation Project (No.2015DFG12150)
- DOI：10.3969/j.issn.0372-2112.2017.07.017
  CLC： TP391
- Published Online：25 July 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
BI Xiao-jun, PAN Tie-wen. Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm[J]. Acta Electronica Sinica, 2017, 45(7): 1668-1676.
DOI：

BI Xiao-jun, PAN Tie-wen. Relevance Feedback Image Retrieval Based on Teaching-learning-based Optimization Algorithm[J]. Acta Electronica Sinica, 2017, 45(7): 1668-1676. DOI： 10.3969/j.issn.0372-2112.2017.07.017.

摘要

为提高基于内容的图像检索的检索性能和检索速度，克服低层视觉特征与高层语义概念间的语义鸿沟，提出一种基于教与学优化的图像检索相关反馈算法（TLBO-RF）.结合图像检索问题的特殊性和粒子群优化算法的优点，对TLBO算法中个体的更新机制进行了改进，通过将相关图像集的中心作为教师以及引入学员最好学习状态Pbest，使之朝用户感兴趣的相关图像区域快速收敛.将该算法与目前效果最好的两种基于进化算法的相关反馈技术在两套标准图像测试集上进行对比，结果表明本文算法相较于另外两种算法具有明显的优势，不仅提高了图像检索性能，同时也加快了图像检索速度，更好地满足了用户的检索要求.

Abstract

To improve the performance of image retrieval

and accelerate the speed of image retrieval in content-based image retrieval and reduce the semantic gap between visual low-level features and high-level semantic

relevance feedback image retrieval based on teaching-learning-based optimization algorithm is proposed (TLBO-RF).Considering the specificity of image retrieval and the advantage of the PSO

the update strategy of individual is modified in TLBO

the center of the relevant images is regarded as the teacher and the personal best is introduced

which makes the algorithm converge fast to the region of relevant images that the user is interested in.TLBO-RF is compared to two state-of-the-art RFs based on evolutionary algorithm on two benchmark images.The results show that TLBO-RF has obvious advantage in comparison with other two algorithms

not only increases the performance of image retrieval

but also improves the image retrieval speed

and can better meet the user needs of image retrieval.

关键词

Keywords

references

Views

339

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Combining Graph Learning and Region Saliency Analysis for Content-Based Image Retrieval

Hierarchical Co-Evolution Immune Algorithm and Its Application on TSP

A Novel Image Retrieval Method Using Texture Features Based on Color-Connected Regions

Related Author

David Dagan FENG

SHEN Lan-sun

ZHANG Jing

XU De

LANG Cong-yan

FENG Song-he

LIU Zhao-hua

ZHANG Xiao-gang

Related Institution

香港理工大学电子与资讯工程学系香港中国

悉尼大学信息学院悉尼 2006澳大利亚

北京工业大学信号与信息处理研究室北京 100022中国

香港理工大学电子与资讯工程学系

悉尼大学信息学院

⁰