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

Author:

Zhu Yaolin12,Duan Jiameng1ORCID,Wu Tong1

Affiliation:

1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China

Abstract

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.

Publisher

SAGE Publications

Subject

General Materials Science

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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3. Financial data Security Protection System Based on Improved Random Forest Algorithm;2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24

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