摘要
对深度学习领域的稠密卷积网络(dense convolutional network,DenseNet)进行改进,提出了一种嵌套网络模型下的相似图像检索方法。该方法主要通过嵌入压缩和激励网络(squeeze-and-excitation network,SENet),调整原DenseNet网络结构,优化特征提取模块,从而提高图像检索的准确率。在整个深度学习的过程中,给图像特征通道设置合理的权值,抑制图像中的无效特征,能够进一步提高图像的检索速度。实验结果表明,所提算法能够加强图像有效特征的传递,无论从精度和速度方面均可得到较好的图像检索结果。
A similar image retrieval method based on nested network model is proposed by improving the traditional dense network(DenseNet)which is a common deep-learning method.First,the proposed algorithm optimizes feature retrieving blocks by embedding squeeze-and-excitation network(SENet)into the original DenseNet and adjusting the structure of the DenseNet,so as to improve the accuracy of image retrieval.Second,the algorithm achieves the speed-up of image retrieval by setting proper weight for each feature channel of the image,thus suppressing the processing time of those invalid features.Experimental results show that the algorithm can strengthen the transmission of effective image features and improve the accuracy of image researching results effectively.
作者
倪翠
王朋
朱元汀
张东
NI Cui;WANG Peng;ZHU Yuanting;ZHANG Dong(School of Information Science and Electric Engineering,Shandong JiaoTong University,Jinan 250357,Shandong,China;Institute of Automation,Shandong Academy of Sciences,Jinan 250014,Shandong,China)
出处
《应用科学学报》
CAS
CSCD
北大核心
2022年第3期400-410,共11页
Journal of Applied Sciences
基金
国家自然科学基金青年基金(No.61502277)
山东省重点研发计划基金(No.2019GHZ006)
山东省科学院科教产融合创新试点工程项目基金(No.2020KJC-GH05)资助
关键词
稠密卷积网络
压缩和激励网络
嵌套
抑制无效特征
图像检索
dense convolutional network(DenseNet)
squeezing-and-excitation network(SENet)
nesting
suppress invalid feature
image retrieval