Interpolating deep spatio-temporal inference network features for image classification

This paper presents a novel approach for image classification, by integrating the concepts of deep machine learning and feature interpolation. In particular, a recently introduced learning architecture, the Deep Spatio-Temporal Inference Network (DeSTIN) [1] is employed to perform feature extraction for support vector machine (SVM) based image classification. Linear interpolation and Newton polynomial interpolation are each applied to support the classification. This approach converts feature sets of an originally low-dimensionality into those of a significantly higher dimensionality while gaining overall computational simplification. The work is tested against the popular MNIST dataset of handwritten digits [2]. Experimental results indicate that the proposed approach is highly promising.