基于卷积神经网络的少数民族头饰识别

传统头饰图片识别方法的特征点由研究人员手工提取,工作量大且准确率低,识别系统存在预处理步骤繁琐、样本要求高等缺点。针对上述问题,文中通过构建卷积神经网络从大量图片数据中自动学习头饰图片的深层特征。文中的CNN模型选用稀疏性较好的ReLU激活函数调整输出,利用反向传播算法(BP算法)优化网络参数,在训练得到的CNN模型后接Softmax分类器进行识别。实验结果表明,系统对头饰图片测试集的识别率达到96.25%,具有良好的识别准确率和识别效率。

Minority Headdress Recognition Based on Convolutional Neural Network

The feature point of the traditional headdress image recognition method was extracted by the researchers manually. The system has some disadvantages including tedious preprocessing steps, high sample requirement and low accuracy. To solve these problems, the convolutional neural network was constructed to learn the deep features from image data. The CNN model selected the ReLU function with better sparsity to adjust the output, and used back propagation algorithm to optimize the network parameters.The softmax classifier was identified after the CNN model. The experimental results showed that the recognition rate of the system to the test set of headdress reached 96.25%. This method was proved to have good recognition accuracy and recognition efficiency.