Data augmentation by combining feature selection and color features for image classification

Kittikhun Meethongjan (Suan Sunandha Rajabhat University)
Vinh Truong Hoang (Ho Chi Minh City Open University)
Thongchai Surinwarangkoon (Suan Sunandha Rajabhat University)

International Journal of Electrical and Computer Engineering
Vol 12, No 6: December 2022

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.