一种应用多储备池回声状态网络的图像语义映射研究  

Research on Image Semantic Mapping with Multiple-Reservoirs Echo State Network

作　　者：王华秋[1] 王斌[1] 聂珍[2] 

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054 [2]重庆理工大学图书馆,重庆400054

出　　处：《现代图书情报技术》2015年第6期41-48,共8页New Technology of Library and Information Service

基　　金：国家社会科学基金一般项目"数字图书馆智能图像检索系统研制"(项目编号:14BTQ053);重庆市研究生教育教学改革研究项目"研究生<大数据挖掘>课程案例与演示系统研制"(项目编号:yjg143090)的研究成果之一

摘　　要：【目的】建立图像低层特征到高层语义的映射,填补图像检索中的"语义鸿沟",以提高检索准确率。【方法】借鉴集成学习思想,将多储备池回声状态网络(MESN)应用于图像语义映射模型中。图像低层特征按照类型划分后,通过不同的储备池训练,并对训练结果进行线性融合。【结果】该模型相对于BP神经网络和传统ESN,平均映射错误率分别下降31.64%和19.28%,查准率分别提高4.56%和1.86%。【局限】储备池参数通过人工设定,未构造参数优化算法。【结论】实验结果证明,将多储备池回声状态网络应用于图像语义映射中是有效的。[Objective] The mapping between low-level visual feature and high-level semantic information is built up to fill the ＂semantic gap＂ of image retrieval and improve accuracy. [Methods] Referring to the idea of ensemble learning, Multiple-Reservoirs Echo State Networks （MESN） is applied to semantic mapping model. After the low-level visual features of images are divided by feature types and trained by different reservoirs, the training results are combined linearly. [Results] Compared to BP Neural Network and traditional Echo State Network, the average error rate of MESN decreases by 31.64% and 19.28% respectively, the precision rate increases 4.56% and 1.86% respectively. [Limitations] The parameters of reservoirs are set artificially. Parameter optimization algorithm isn＇t constructed. [Conclusions] Experimental results show that the semantic mapping model of Echo State Networks with Multiple-Reservoirs is effective.

关 键 词：图像语义 回声状态网络 多储备池 集成学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]