Image Classification and Retrieval of TCM Materials Based on Feature Enhancement

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 feature points was used to perform coarse classification of the TCM material images; third, clustering algorithms were employed to encode features and perform initial sorting; and finally, the image retrieval results were optimized through reordering based on the initial sorting.Experimental results demonstrate that the methods effectively enhance the classification accuracy and retrieval efficiency of TCM material images.