Interpolating deep spatio-temporal inference network features for image classification

Yongfeng Zhang, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


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.
Original languageEnglish
Title of host publication2014 International Join Conference on Neural Networks (IJCNN)
PublisherIEEE Press
ISBN (Print)978-1-4799-6627-1
Publication statusPublished - 2014


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