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

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

Deep Convolutional Neural Networks-Based Plants Diseases Detection Using Hybrid Features

With advances in information technology, various ways have been developed to detect diseases in plants, one of which is by using Machine Learning. In machine learning, the choice of features affect the performance significantly. However, most features have limitations for plant diseases detection. For that reason, we propose the use of hybrid features for plant diseases detection in this paper. We append local descriptor and texture features, i.e. linear binary pattern (LBP) to color features. The hybrid features are then used as inputs for deep convolutional neural networks (DCNN) Support and VGG16 classifiers. Our evaluation on Based on our experiments, our proposed features achieved better performances than those of using color features only. Our results also suggest fast convergence of the proposed features as the good performance is achieved at low number of epoch

Application of Transfer Learning to Deep CNN for Facial Expression Recognition

We present a web-based application based on facial expression recognition. We investigated five classifiers which include three statistical linear classifiers and two neural network classifiers using three databases (Extended Cohn Kanade (CK+), JAFFE, SFEW), in order to find out the most suitable module for the web application. Based on our initial considerations on the neural networks applied in facial expression recognition, we chose several convolution neural network (CNN) structures investigated by Audre Teixeira Lopes and Minchul Shin. We conducted experiments with detailed training procedures with different classifiers on different datasets to gain comparative results. We chose flask as the web application’s framework because it provides us with a convenient interface for embedding existing python code such as our expression recognition classifier