Color Image Classifier Based on Two-Stage Learning Autoencoder

Analyzing colors in images is an approach to understanding semantic meaning. However, existing research often faces challenges due to limited dataset sizes or the need to create custom datasets. Insufficient data can lead to overfitting during model training and hinder generalization. To address this, we propose a two-stage machine learning method that leverages Kobayashi's Color Image Scale (CIS), a publicly available color image dataset, to enhance the predictive accuracy of color image classifier. In our method, we not only extract colors and image categories as features but also capture the xy-coordinates of color schemes from the CIS. These coordinates play a significant role in improving the accuracy of color image classification. Our two-stage learning method provides a straightforward and effective solution for enhancing predictive accuracy. Through our method, we achieve an impressive 97.36% accuracy in color image classification on the test dataset.