Extraction of Color Information Array from RGB-NIR Images Enhanced by Multispectral Illumination and Image Classification by LLGMN

In recent years, advancements in image classi- fication technology have led to significant improvements in classification accuracy. However, challenges remain, such as difficulties in image classification when redundant information is present, even when using state-of-the-art deep learning methods, and the need for large amounts of training samples for deep learning models. To address these issues, we proposes a method that enhances color information for image classification by combining multi-illumination and multispectral cameras, and utilizes log-linearized gaussian mixture neural network that can classify images with a small number of training samples. The proposed system utilizes a multi-spectral camera capable of capturing Red (R), Green (G), Blue (B), and Near- Infrared (NIR) images (RGB-NIR), along with corresponding multi-spectral illumination. By enhancing color information and clarifying differences in object features, this approach enables high-accuracy color classification with an unprecedent- edly small dataset when input into the log-linearized gaussian mixture neural network. Experimental results demonstrated the effectiveness of the proposed system, achieving 100% color classification accuracy on green tea samples with similar color features. This achievement is expected to contribute to various fields such as manufacturing, healthcare, chemistry, and agriculture, where multispectral imaging is increasingly utilized.