Data Augmentation Based on Color Features for Limited Training Texture Classification
H. T. Duong, Vinh Truong Hoang · 2019
Image classification problem requires a large number of labeled training images to obtain a good generalization. However, it is very time consuming and expensive to label data. The color information has been demonstrated that it contains relevant information for characterizing texture image. This paper presents a novel data augmentation method dedicated to color texture classification. We firstly use color features extracted from different color spaces to augment data in case of one sample training per class. The experimental results is very promising since it significantly improves the classification performance on four benchmark color texture datasets.