Texture classification based on curvelet transform and extreme learning machine with reduced feature set

Abstract

In this work, a novel approach for texture classification is proposed. We present a highly discriminative and simple descriptor to achieve feature learning and classification simultaneously for texture classification. The proposed method introduces the application of digital curvelet transform and explores feature reduction properties of locality sensitive discriminant analysis (LSDA) in conjunction with extreme learning machine (ELM) classifier. The image is mapped to the curvelet space. However, the curse of dimensionality problem arises when using the curvelet coefficients directly and therefore a reduction method is required. LSDA is used to reduce the data dimensionality to generate relevant features. These reduced features are used as the input to ELM classifier to analytically learn an optimal model. In contrast to traditional methods, the proposed method learns the features by the network itself and can be applied to more general applications. Extensive experiments conducted in two different domains using two benchmark databases, illustrate the effectiveness of the proposed method. In addition, empirical comparisons of the proposed method against curvelet transform in conjunction with traditional dimensionality reduction tools show that the suggested method does not only lead to a more reduced feature set, but it also outperforms all the compared methods in terms of accuracy.