基于显著性语义区域加权的图像检索算法  

Image retrieval algorithm based on saliency semantic region weighting

作　　者：陈宏宇 邓德祥[1] 颜佳[1] 范赐恩[1] CHEN Hongyu;DENG Dexiang;YAN Jia;FAN Ci’en(Electronic Information School,Wuhan University,Wuhan Hubei 430072,China)

机构地区：[1]武汉大学电子信息学院,武汉430072

出　　处：《计算机应用》2019年第1期136-142,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61701351)~~

摘　　要：针对计算视觉领域图像实例检索的问题,提出了一种基于深度卷积特征显著性引导的语义区域加权聚合方法。首先提取深度卷积网络全卷积层后的张量作为深度特征,并利用逆文档频率(IDF)方法加权深度特征得到特征显著图;然后将其作为约束,引导深度特征通道重要性排序以提取不同特殊语义区域深度特征,排除背景和噪声信息的干扰;最后使用全局平均池化进行特征聚合,并利用主成分分析(PCA)降维白化得到图像的全局特征表示,以进行距离度量检索。实验结果表明,所提算法提取的图像特征向量语义信息更丰富、辨识力更强,在四个标准的数据库上与当前主流算法相比准确率更高,鲁棒性更好。For image instance retrieval in the field of computational vision, a semantic region weighted aggregation method based on significance guidance of deep convolution features was proposed. Firstly, a tensor after full convolutional layer of deep convolutional network was extracted as deep feature. A feature saliency map was obtained by using Inverse Document Frequency( IDF) method to weight deep feature, and then it was used as a constraint to guide deep feature channel importance ordering to extract different special semantic region deep feature, which excluded interference from background and noise information. Finally, global average pooling was used to perform feature aggregation, and global feature representation of image was obtained by using Principal Component Analysis( PCA) to reduce the dimension and whitening for distance metric retrieval. The experimental results show that the proposed image retrieval algorithm based on significant semantic region weighting is more accurate and robust than the current mainstream algorithms on four standard databases, because the image feature vector extracted by the proposed algorithm is richer and more discerning.

关 键 词：图像检索 卷积神经网络 深度特征显著性 语义区域加权 特征聚合 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]