传统图像标注方法中人工选取特征费时费力,传统标签传播算法忽视语义近邻,导致视觉相似而语义不相似,影响标注效果.针对上述问题,文中提出融合深度特征和语义邻域的自动图像标注方法.首先构建基于深度卷积神经网络的统一、自适应深度特征提取框架,然后对训练集划分语义组并建立待标注图像的邻域图像集,最后根据视觉距离计算邻域图像各标签的贡献值并排序得到标注关键词.在基准数据集上实验表明,相比传统人工综合特征,文中提出的深度特征维数更低,效果更好.文中方法改善传统视觉近邻标注方法中的视觉相似而语义不相似的问题,有效提升准确率和准确预测的标签总数.%In the traditional image annotation methods,the manual selection of features is time-consuming and laborious.In the traditional label propagation algorithm,semantic neighbors are ignored.Consequently visual similarity and semantic dissimilarity are caused and annotation results are affected.To solve these problems,an automatic image annotation method combining semantic neighbors and deep features is proposed.Firstly,a unified and adaptive depth feature extraction framework is constructed based on deep convolutional neural network.Then,the training set is divided into semantic groups and the neighborhood image sets of the unannotated images are set up.Finally,according to the visual distance,the contribution value of each label of the neighborhood images is calculated and the keywords are obtained by sorting their contribution values.Experiments on benchmark datasets show that compared with the traditional synthetic features,the proposed deep feature possesses lower dimension and better effect.The problem of visual similarity and semantic dissimilarity in visual nearest neighbor annotation method is improved,and the algorithm effectively enhances the accuracy and the number of accurate predicted tags.
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