Enhanced extreme learning machines for image classification

Abstract

Image Classification is one of the key computer vision tasks. Among numerous machine learning methods, we choose the Extreme Learning Machine (ELM) for our image classification applications. This thesis contributes to four aspects of ELM netwroks. From the view of efficient input data, we have designed handcrafted feature extraction method for smile images classification. From the perspective of the distribution of random weights between the input layer and hidden layer, we have proposed and proved the effectiveness of the sparse binary ELM. Inspired by the deep architecture of deep learning, we have extended the single layer to multiple layers of ELM to achieve better performance on large image classification datasets. Finally, from the point of target coding, we have introduced and evaluated different target coding methods for image classification.