Combining Newton interpolation and deep learning for image classification

A novel approach for image classification, by integrating deep learning and feature interpolation, supported with advanced learning classification techniques, is presented. The recently introduced deep spatiotemporal inference network (DeSTIN) is employed to carry out limited original feature extraction. Newton interpolation is then used to artificially increase the dimensionality of the extracted feature sets for accurate classification, without incurring heavy computational cost. Support vector machines are utilised for image classification. The proposed approach is tested against the popular MNIST dataset of handwritten digits, demonstrating the potential of the approach.