An Image Retrieval Method Based on r/KPSO

Xu Zhang, B. Guo, Guiyue Zhang, Yunyi Yan
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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.
基于r/KPSO的图像检索方法
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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