Image Classification and Retrieval of TCM Materials Based on Feature Enhancement

作     者：Zhu, Xianghe 

作者机构：Hubei Univ Chinese Med Coll Informat Engn Wuhan 430065 Peoples R China Hubei Shizhen Lab Wuhan 430000 Peoples R China 

出 版 物：《TRAITEMENT DU SIGNAL》 (Trait. Signal)

年 卷 期：2024年第41卷第6期

页      面：3223-3233页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Doctoral Research Launch Project of Hubei University of Chinese Medicine [2023ZXB027] Collaborative Education Project for Industry-University Cooperation, Ministry of Education 

主　　题：TCM materials image classification image retrieval superpixel segmentation feature extraction clustering algorithm reordering 

摘      要：With the global promotion and application of Traditional Chinese Medicine (TCM), the identification and management of TCM materials have become critical issues that need to be addressed. Traditional methods for identifying TCM materials rely on manual experience and expert knowledge, leading to low efficiency and a high likelihood of errors. With the development of image processing technology, image-based classification and retrieval of TCM materials have gradually become a research hotspot. However, existing methods often encounter challenges such as insufficient classification accuracy and low retrieval efficiency when faced with the diversity and complexity of TCM material images. Therefore, how to effectively extract image features and improve the accuracy of classification and retrieval has become the central challenge in current research. Traditional image features, such as color, shape, and texture, are commonly used in the classification and retrieval of TCM materials. However, these features are often unable to fully reflect the diversity and detail of the materials, especially when distinguishing between morphologically similar materials. Although deep learning techniques have made breakthroughs in the field of image processing, the application of deep learning in TCM material image classification still faces many challenges due to insufficient data and annotation. A combination of technologies, including superpixel segmentation, feature point extraction, and clustering encoding, provides an effective approach to improving classification and retrieval performance and warrants further research. A kind of feature enhancement-based method for the classification and retrieval of TCM material images was proposed in this study, consisting of four main components. First, fine image segmentation was performed using the Simple Linear Iterative Clustering (SLIC) superpixel segmentation technique to extract features;second, an initial classification method based on f