Animal fiber imagery classification using a combination of random forest and deep learning methods

• Published in: Journal of engineered fibers and fabrics Vol. 16
• Main Authors: Zhu, Yaolin, Duan, Jiameng, Wu, Tong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2021
• Subjects: Extremely similar animal fibers; convolutional neural networks; random forest; image classification
• ISSN: 1558-9250; 1558-9250
• DOI: 10.1177/15589250211009333

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.