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

<span lang="EN-US">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 required to have better model. However, it true that full label costly obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead degrade the performance. this paper, we propose method augmentation based on color features information combining feature selection. combination allows improving accuracy. The proposed approach evaluated several texture datasets using local binary patterns features.</span>