融合迁移学习的Inception-v3模型在古壁画朝代识别中的应用

针对古代壁画图像数量少、质量差、特征提取困难和存在壁画文本与绘画风格相似等问题，提出了一种融合迁移学习的Inception-v3模型来对古代壁画的朝代进行识别与分类。首先，将Inception-v3模型在ImageNet数据集上进行预训练以得到迁移模型；然后，将迁移模型在小型壁画数据集上进行参数微调后对壁画图像提取高层特征；其次，增加两个全连接层来增强特征表达能力，并用颜色直方图与局部二值模式（LBP）纹理直方图提取壁画的艺术特征；最后，将高层特征与艺术特征相融合，用Softmax分类器进行壁画的朝代分类。实验结果表明，所提出的模型训练过程稳定，在构造的小型壁画数据集上，其最终准确率为88.70%，召回率为88.62%，F1值为88.58%，以上各评价指标均优于AlexNet、VGGNet等经典网络模型；与LeNet-5、AlexNet-S6等改进的卷积神经网络模型相比，该模型对各朝代类别准确率平均提升了至少7个百分点。可见，该模型泛化能力强，不易出现过拟合现象，能有效识别壁画所属朝代。

Application of Inception-v3 model integrated with transfer learning in dynasty identification of ancient murals

Aiming at the problems of small quantity， poor quality， difficulty in feature extraction， and similarity of mural text and painting style of ancient mural images， an Inception-v3 model integrated with transfer learning was proposed to identify and classify the dynasties of ancient murals. Firstly， the Inception-v3 model was pre-trained on the ImageNet dataset to obtain the migration model. After fine-tuning the parameters of the migration model on the small mural dataset， the high-level features were extracted from the mural images. Then， the feature representation ability was enhanced by adding two fully connected layers， and the color histogram and Local Binary Pattern （LBP） texture histogram were used to extract the artistic features of murals. Finally， the high-level features were combined with the artistic features， and the Softmax classifier was used to perform the dynasty classification of murals. Experimental results show that， the training process of the proposed model was stable. On the constructed small mural dataset， the proposed model has the final accuracy of 88.70%， the recall of 88.62%， and the F1-score of 88.58%. Each evaluation index above of the proposed model is better than those of the classic network models such as AlexNet and Visual Geometry Group Net （VGGNet）. Compared with LeNet-5， AlexNet-S6 and other improved convolutional neural network models， the proposed model has the accuracy of each dynasty category improved by at least 7 percentage points on average. It can be seen that the proposed model has strong generalization ability， is not prone to overfitting， and can effectively identify the dynasty to which the murals belong.