Research on Image Semantic Mapping with Multiple-Reservoirs Echo State Network
Wang Huaqiu1, Wang Bin1, Nie Zhen2
1 College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China;
2 Chongqing University of Technology Library, Chongqing 400054, China
[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.
王华秋, 王斌, 聂珍. 一种应用多储备池回声状态网络的图像语义映射研究[J]. 现代图书情报技术, 2015, 31(6): 41-48.
Wang Huaqiu, Wang Bin, Nie Zhen. Research on Image Semantic Mapping with Multiple-Reservoirs Echo State Network. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.06.07.
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