{"title":"An Image Retrieval Method Based on r/KPSO","authors":"Xu Zhang, B. Guo, Guiyue Zhang, Yunyi Yan","doi":"10.1109/IBICA.2011.22","DOIUrl":null,"url":null,"abstract":"Image retrieval is a hot and hard technology in the field of computing science. In this paper, a method named r/KPSO (Particle Swarm Optimization with r- and K-selection) is applied in relevance feedback (RF) of image retrieval. The main idea of r/KPSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. Based on r/KPSO, we define the positive and negative feedback samples as study principle, and optimize weightings according to user's retrieval requirement. Experiments show that both the recall and precision are improved effectively.","PeriodicalId":158080,"journal":{"name":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBICA.2011.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Image retrieval is a hot and hard technology in the field of computing science. In this paper, a method named r/KPSO (Particle Swarm Optimization with r- and K-selection) is applied in relevance feedback (RF) of image retrieval. The main idea of r/KPSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. Based on r/KPSO, we define the positive and negative feedback samples as study principle, and optimize weightings according to user's retrieval requirement. Experiments show that both the recall and precision are improved effectively.
图像检索是计算机科学领域的一个热点和难点技术。本文将r/KPSO (Particle Swarm Optimization with r- and K-selection)方法应用于图像检索的相关反馈。r/KPSO的主要思想受到生态学r-和k -选择的启发。r-选择表现为数量多、亲本照顾少、生长率大、发育快;k -选择表现为质量多、亲本照顾多、生长率小、发育慢。在r/KPSO的基础上,定义正负反馈样本作为研究原则,并根据用户的检索需求优化权重。实验表明,该方法有效地提高了查全率和查准率。