Enhanced extreme learning machines for image classification

Image Classification is one of the critical computer vision tasks, and it is also the foundation of the related tasks like object detection/recognition/segmentation, which all need to identify the positive and negative samples. Methods proposed for image classification can be divided into two groups: traditional image processing based methods and modern machine learning based methods. The main difference between these two categories is how to extract the features, including the common features of the intraclass samples and the distinguishing features of the interclass samples. For traditional image processing based methods, researchers focus on extracting features manually after the mere observation of the samples. While for modern machine learning based methods, and researchers tend to focus on designing networks based on the training data, building a model, and evaluating the model on the test data.