Feature extraction is a key step in animal fiber microscopic images recognition that plays an important role in the wool industry and textile industry. To improve the accuracy of wool and cashmere microscopic images classification, a hybrid model based on Convolutional Neural Network (CNN) and Random Forest (RF) is proposed for automatic feature extraction and classification of animal fiber microscopic images. First, use CNN to learn the representative high-level features from animal fiber images, then add dropout layers to avoid over-fitting. And the backward propagation algorithm are used to optimize the CNN structure. Random forest, which is robust and has strong generalization ability, is introduced for the classification of animal fiber microscopic images to obtain the final results. The study shows that, the proposed method has better generalization performance and higher classification accuracy than other classification methods.

特征提取是动物纤维显微图像识别中的关键步骤，在羊毛工业和纺织工业中起着重要作用。为了提高羊毛和羊绒显微图像分类的准确性，提出了一种基于卷积神经网络（CNN）和随机森林（RF）的混合模型，用于动物纤维显微图像的自动特征提取和分类。首先，使用CNN从动物纤维图像中学习代表性的高级特征，然后添加辍学图层以避免过度拟合。然后使用反向传播算法来优化CNN结构。引入了鲁棒性强，泛化能力强的随机森林对动物纤维显微图像进行分类，以获得最终结果。研究表明，