数字图书馆论坛Issue(2)：45-51,7.DOI:10.3772/j.issn.1673-2286.2018.02.008
基于相关反馈的特征融合图像检索优化策略初探
Image Retrieval Based on Relevance Feedback
摘要
Abstract
Introducing the principle of the multi-feature integration image retrieval based on the automatic semantic annotation by means of the relevance feedback, and summarizing the further development and the limitations, which are particular stress on the less emphasis on the query keywords the users input, the outdated images, over-feedback. Based on these limitations and aiming at improving user experience, the paper proposes a series of improvement methods, which respectively are a method to improve the result ranking is proposed based on the particular attribute-nonidentity of information recourse, the optimization of the updating the feature words' weights, weighting the keywords in the query. These strategies can improve the user experience, optimize the updating the feature words' weights and alleviate the problem of over-feedback and other preceding limitations. Especially, the result ranking based on the nonidentity can accelerate the access of the diverse desired images and improve the accuracy of the semantic web to describe the semantic feature of the images.关键词
相关反馈/图像检索/特征融合/不同一性Key words
Relevance Feedback/Image Retrieval/Multi-Feature Integration/Nonidentity分类
信息技术与安全科学引用本文复制引用
黄莺..基于相关反馈的特征融合图像检索优化策略初探[J].数字图书馆论坛,2018,(2):45-51,7.基金项目
本研究得到西南民族大学2016年度中央高校基本科研业务费专项资金项目"智慧政府战略中政务数据开放机制与平台建设研究"(编号:2016NZYQN26)资助. （编号:2016NZYQN26）
