The Performance Research of the Data Augmentation Method for Image Classification

Abstract

To collect full-labeled data is a challenge problem for learning classifiers. Nowadays, the general tendency of developing a model is becoming larger to be able to obtain more potential capacity to effectively predict unknown instances. However, imbalanced datasets still are not able to meet the needs for training a robustness classifier. A convincing guidance to extract invariance features from images is training in augmented input datasets. However, selecting a proper way to generate synthetic samples from a larger quality of feasible augmentation methods is still a big challenge. In the paper, we use three types of datasets and investigate the merits and demerits of five image transformation methods - color manipulate methods (color and contrast) and traditional affine transformation (shift, rotation, and flip). We found a common experiment result that plausible color transformation methods perform worse against traditional affine transformations in solving the overfitting problem and improve the classification accuracy.